-
-
-
+
+
-The R package `SplineOmics` finds the significant features (hits) of time-series -omics data by using splines and `limma` for hypothesis testing. It then clusters the hits based on the spline shape while showing all results in summary HTML reports.
+The R package `SplineOmics` finds the significant features (hits) of
+time-series -omics data by using splines and `limma` for hypothesis
+testing. It then clusters the hits based on the spline shape while
+showing all results in summary HTML reports.
-The graphical abstract below shows the full workflow streamlined by `SplineOmics`:
+The graphical abstract below shows the full workflow streamlined by
+`SplineOmics`:
-
-
+Usually, the final step in such a bioinformatics analysis is GSEA. To -each clustered hit, the respective gene can be assigned and GSEA -performed. For this, the Enrichr databases of choice have to be -downloaded:
+Once the clustered hits are identified, a subsequent step to gain +biological insights is to perform GSEA. For this, the respective genes +can be assigned to each clustered hit, and GSEA can be carried out. To +proceed, the Enrichr databases of choice need to be downloaded:
# Specify which databases you want to download from Enrichr
gene_set_lib <- c(
diff --git a/docs/articles/get-started.html b/docs/articles/get-started.html
index 5851d0e..a770e8f 100644
--- a/docs/articles/get-started.html
+++ b/docs/articles/get-started.html
@@ -954,10 +954,10 @@ Cluster the hits (significant fea
Perform gene set enrichment analysis (GSEA)
-Usually, the final step in such a bioinformatics analysis is GSEA. To
-each clustered hit, the respective gene can be assigned and GSEA
-performed. For this, the Enrichr databases of choice have to be
-downloaded:
+Once the clustered hits are identified, a subsequent step to gain
+biological insights is to perform GSEA. For this, the respective genes
+can be assigned to each clustered hit, and GSEA can be carried out. To
+proceed, the Enrichr databases of choice need to be downloaded:
# Specify which databases you want to download from Enrichr
gene_set_lib <- c(
diff --git a/docs/index.html b/docs/index.html
index ae73e5b..0a4c614 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -68,8 +68,8 @@
The R package SplineOmics finds the significant features (hits) of time-series -omics data by using splines and limma for hypothesis testing. It then clusters the hits based on the spline shape while showing all results in summary HTML reports.
The graphical abstract below shows the full workflow streamlined by SplineOmics:
-
-Graphical Abstract of SplineOmics Workflow
+
+Graphical Abstract of SplineOmics Workflow
Table of Contents
@@ -167,8 +167,7 @@ Installation Steps
-# Define the custom library path and expand the tilde (~)
-custom_lib_path <- path.expand("~/Rlibs")
+custom_lib_path <- "C:/Rlibs" # Replace with your desired path
# Create the directory if it doesn't exist
if (!dir.exists(custom_lib_path)) {
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 9df3c53..6b62481 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
Docker-instructions: Docker-instructions.html
get-started: get-started.html
RNA-seq analysis: RNA-seq analysis.html
-last_built: 2025-01-13T13:44Z
+last_built: 2025-01-13T15:25Z
urls:
reference: https://csbg.github.io/SplineOmics/reference
article: https://csbg.github.io/SplineOmics/articles
diff --git a/docs/search.json b/docs/search.json
index 5cceb15..2700c75 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":null,"dir":"","previous_headings":"","what":"Changelog","title":"Changelog","text":"notable changes project documented file. format based Keep Changelog, project adheres Semantic Versioning.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"template-for-changelog-entries","dir":"","previous_headings":"","what":"Template for Changelog Entries","title":"Changelog","text":"release section follow structure :","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"added","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Added","title":"Changelog","text":"New features functionality introduced release.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"changed","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Changed","title":"Changelog","text":"Updates modifications existing features.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"fixed","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Fixed","title":"Changelog","text":"Bugs resolved.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"deprecated","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Deprecated","title":"Changelog","text":"Features still functional slated removal future.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"removed","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Removed","title":"Changelog","text":"Features functionality removed.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"security","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Security","title":"Changelog","text":"security-related updates patches. Examples: - Added: Introduced new plotting function plotTimeSeries(). - Fixed: Resolved issue causing crashes input data missing values. - Changed: Modified default parameters normalizeData().","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing othersβ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement thomas.rauter@plus.ac.. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozillaβs code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"pulling-the-docker-container","dir":"Articles","previous_headings":"","what":"Pulling the Docker Container","title":"Docker-instructions","text":"pull Docker container, use following command. Make sure check newest version specific version need visiting Docker Hub repository. face βpermission deniedβ issues, check vignette","code":"docker pull thomasrauter/splineomics:0.1.0"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"running-the-docker-container","dir":"Articles","previous_headings":"","what":"Running the Docker Container","title":"Docker-instructions","text":"run Docker container, can use one following commands, depending operating system. running command, ensure directory containing two subfolders: input output. used transfer files local machine Docker container. Linux macOS (Bash): Windows (PowerShell): container running, open web browser navigate http://localhost:8888. Log using following credentials: Username: rstudio Password: one set -e PASSWORD=123 option (123 case) long container running, can work localhost page RStudio, also SplineOmics package installed. /home/rstudio/ R session working folder. Stop container: Start container :","code":"docker run -it -d \\ -v $(pwd)/input:/home/rstudio/input \\ -v $(pwd)/output:/home/rstudio/output \\ -p 8888:8787 \\ -e PASSWORD=123 \\ --name splineomics \\ thomasrauter/splineomics:0.1.0 docker run -it -d ` -v \"${PWD}\\input:/home/rstudio/input\" ` -v \"${PWD}\\output:/home/rstudio/output\" ` -p 8888:8787 ` -e PASSWORD=123 ` --name splineomics ` thomasrauter/splineomics:0.1.0 docker stop splineomics docker start splineomics"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"using-the-docker-desktop-gui-instead","dir":"Articles","previous_headings":"","what":"Using the Docker Desktop gui instead","title":"Docker-instructions","text":"Instead pasting commands Power- Bashshell, can also graphical user interface Docker Desktop.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"input-and-output-file-management","dir":"Articles","previous_headings":"","what":"Input and Output File Management","title":"Docker-instructions","text":"input output directories local machine mounted corresponding directories inside Docker container. allows seamless file transfer local machine container. Place input files (e.g., data, metadata, annotation files) input directory local machine. files automatically appear /home/rstudio/input inside container. files generated RStudio within container saved /home/rstudio/output. files automatically appear output directory local machine.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"inspect-docker-container-installations","dir":"Articles","previous_headings":"","what":"Inspect Docker container installations","title":"Docker-instructions","text":"see R packages system installations make Docker container, can run following command terminal RStudio localhost browser page. /home/rstudio/output dir mounted local filesystem, make installation log files available .","code":"cp -r /log home/rstudio/output"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"installing-additional-r-packages-in-the-container","dir":"Articles","previous_headings":"","what":"Installing additional R packages in the container","title":"Docker-instructions","text":"New R packages can installed normal way: However, note packages installed running container lost container deleted rebuilt.","code":"install.packages(\"package_name\")"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"permanent-additions","dir":"Articles","previous_headings":"","what":"Permanent additions","title":"Docker-instructions","text":"want permanently add R packages, R scripts, files SplineOmics Docker image, can use base image building new image. ensure changes saved new image, rather lost container deleted. example: Run container new image commands described .","code":"# Use the SplineOmics image as the base image FROM thomasrauter/splineomics:0.1.0 # Install the data.table package permanently RUN R -e \"install.packages('data.table')\" # Optionally, add custom R scripts to the image COPY your_script.R /home/rstudio/your_script.R # Set the working directory WORKDIR /home/rstudio # Expose RStudio Server port EXPOSE 8787 # Start RStudio server CMD [\"/init\"] # Build new image: # docker build -t your_new_image_name ."},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"creating-a-reproducible-docker-container-with-automated-analysis","dir":"Articles","previous_headings":"","what":"Creating a Reproducible Docker Container with Automated Analysis","title":"Docker-instructions","text":"final analysis script inside Docker container SplineOmics package, want scientists can easily reproduce results running just one line code, follow guide . instruct create new image based container, can save example Docker Hub. Others can download image, run container get exact results got.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"prepare-your-analysis-and-scripts","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"1. Prepare your analysis and scripts","title":"Docker-instructions","text":"Ensure analysis scripts necessary files saved dedicated directory inside container (e.g., /home/rstudio/analysis/). analysis script take input files directory like /home/rstudio/input/ (already inside container need mounted reproducing analysis) output results /home/rstudio/output/. /home/rstudio/output/ directory mounted local directory userβs machine, making results accessible outside container. Example directory structure:","code":"/home/rstudio/ βββ analysis/ βββ final_analysis.R # Main analysis script βββ helper_functions.R # Supporting scripts"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"create-an-entry-point-script","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"2. Create an Entry Point Script","title":"Docker-instructions","text":"Create bash script (run_analysis.sh) runs analysis automatically. Example run_analysis.sh: Save script /home/rstudio/.","code":"#!/bin/bash Rscript /home/rstudio/analysis/final_analysis.R tail -f /dev/null # Keep the container running after analysis"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"commit-the-container-as-a-new-image-with-an-entry-point","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"3. Commit the Container as a New Image with an Entry Point","title":"Docker-instructions","text":"scripts ready, commit running container new image set new entry point run bash script automatically:","code":"docker commit \\ --change='CMD [\"/bin/bash\", \"/home/rstudio/run_analysis.sh\"]' \\ \\ thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"push-the-new-image-to-docker-hub","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"4. Push the New Image to Docker Hub","title":"Docker-instructions","text":"Push new image Docker Hub others can easily pull reproduce analysis: Others can pull (download) container command:","code":"docker push thomasrauter/splineomics-analysis:v1 docker pull thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"running-the-container-to-reproduce-the-results","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"5. Running the container to reproduce the results","title":"Docker-instructions","text":"reproduce results, need create local directory results saved mount directory containerβs /home/rstudio/output/ directory. Use following command run container ensure results saved local output directory (see commands section Running Docker Container mount output dir current working dir).","code":"docker run -it \\ -v /path/to/local/output:/home/rstudio/output \\ thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"optional-getting-insights-into-the-full-analysis","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"(Optional) Getting insights into the full analysis","title":"Docker-instructions","text":"Start new container mount empty local directory /home/rstudio/ directory inside container. allows directly access analysis files local machine.","code":"docker run -it \\ -v /path/to/local/dir:/home/rstudio \\ thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"about-this-vignette","dir":"Articles","previous_headings":"","what":"About this vignette","title":"RNA-seq analysis","text":"tutorial intends showcase explain capabilities SplineOmics package walking real complete RNA-seq example, start finish. SplineOmics explained detail get-started vignette, proteomics example covered. vignette focused showing RNA-seq data can used, , less details overall package provided .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"data-overview","dir":"Articles","previous_headings":"About this vignette","what":"Data Overview","title":"RNA-seq analysis","text":"dataset originates time-series RNA-seq experiment designed study Chinese Hamster Ovary (CHO) cells. experiment involved cultivating cells eight bioreactors, four bioreactors subjected temperature shift 146 hours (experimental condition) remaining four bioreactors maintained without temperature shift (control condition). Samples collected 17 distinct time points throughout experiment, specifically: \"72h\", \"76h\", \"96h\", \"120h\", \"124h\", \"144h\", \"148h\", \"152h\", \"168h\", \"192h\", \"216h\", \"220h\", \"240h\", \"264h\", \"268h\", \"288h\", \"312h\" cultivation start. time point sampled eight bioreactors, resulting total 136 samples. two batch effects: Reactor Plate. Different bioreactors, cells cultivated, can show big variablity. Plate refers two different plates used RNA-seq analysis samples. fully random design used distribute samples two plates. vignette, just remove plate batch effect. data matrix comprises genes rows samples columns, providing gene expression measurements time points. sample initially sequenced three technical replicates across two NovaSeq X flow cells. technical replicates collapsed generate final dataset used analysis. goal experiment investigate effect temperature shift CHO cell cultivation gene expression dynamics time. Note: original dataset, yet published time vignetteβs creation. demonstration purposes, genes randomly shuffled, subset data included reduce dataset size.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"analysis-goals","dir":"Articles","previous_headings":"About this vignette","what":"Analysis Goals","title":"RNA-seq analysis","text":"main objectives analysis : Identify genes significant temporal changes: Among thousands genes measured, goal identify exhibit significant changes expression time. Cluster genes based temporal patterns: Genes showing significant temporal changes (hits) grouped clusters based time-dependent expression patterns. Perform gene set enrichment analysis: cluster, gene set enrichment analysis conducted identify whether specific biological pathways processes - downregulated response feeding processes influenced temperature shift. Assess impact temperature shifts temporal patterns: analysis determine whether temporal patterns gene expression affected temperature shift, .e., whether gene expression dynamics differ time temperature shift conditions compared controls.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"load-the-packages","dir":"Articles","previous_headings":"","what":"Load the packages","title":"RNA-seq analysis","text":"","code":"library(SplineOmics) #> Warning: replacing previous import 'limma::topTable' by #> 'variancePartition::topTable' when loading 'SplineOmics' library(readr) # For reading the meta CSV file library(here) # For managing filepaths #> here() starts at /tmp/RtmpDa5Umr library(dplyr) # For data manipulation #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(knitr) # For Showing the head of the data and the meta tables."},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"load-the-files","dir":"Articles","previous_headings":"","what":"Load the files","title":"RNA-seq analysis","text":"","code":"data <- readRDS(xzfile(system.file( \"extdata\", \"rna_seq_data.rds.xz\", package = \"SplineOmics\" ))) meta <- readr::read_csv( system.file( \"extdata\", \"rna_seq_meta.csv\", package = \"SplineOmics\" ), show_col_types = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"show-top-rows-of-data","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of data","title":"RNA-seq analysis","text":"data: numeric matrix row represents gene (features) column corresponds sample. row names matrix contain gene identifiers, columns aligned sample metadata meta. matrix contains expression values 136 samples. Note study conducted blinded manner, samples randomly distributed across two plates RNA-seq analysis. result, sample numbers (e.g., 1, 2, 3, etc.) sequential order respect time, condition, plate. data analysis involving splines time, essential sort samples based time establish valid temporal sequence. Additionally, organizing data way improves clarity ensures consistency. Within time point, samples sorted condition (e.g., constant temp_shift) , subsequently, plate (e.g., plate_1 plate_2).","code":"kable( head(data), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"show-top-rows-of-meta","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of meta","title":"RNA-seq analysis","text":"meta: data frame containing metadata information samples data. row meta corresponds column data, ensuring 1:1 alignment metadata entries expression data samples. columns meta describe various attributes samples, SampleNr, Reactor, Time, Condition, Plate.","code":"kable( head(meta), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"preprocess-the-data","dir":"Articles","previous_headings":"","what":"Preprocess the data","title":"RNA-seq analysis","text":"Filter data rows (genes) zero counts across samples. step standard preprocessing procedure RNA-seq data analysis, genes zero counts samples provide information downstream analyses.","code":"rows_before <- nrow(data) # Filter data rows data <- data[rowSums(data) > 0, ] rows_after <- nrow(data) rows_removed <- rows_before - rows_after cat(sprintf( \"Rows before filtering: %d\\nRows after filtering: %d\\nRows removed: %d\\n\", rows_before, rows_after, rows_removed )) #> Rows before filtering: 1000 #> Rows after filtering: 944 #> Rows removed: 56"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"perform-eda-exploratory-data-analysis","dir":"Articles","previous_headings":"","what":"Perform EDA (exploratory data analysis)","title":"RNA-seq analysis","text":"can see HTML report explore_data() function batch-corrected data, report batch-corrected data.","code":"report_info <- list( omics_data_type = \"RNA\", data_description = \"RNA-seq data of CHO cells\", data_collection_date = \"December 2024\", analyst_name = \"Thomas Rauter\", contact_info = \"thomas.rauter@plus.ac.at\", project_name = \"DGTX\" ) report_dir <- here::here( \"results\", \"explore_data\" ) splineomics <- SplineOmics::create_splineomics( data = data, meta = meta, report_info = report_info, condition = \"Condition\", # Column of meta that contains the levels. meta_batch_column = \"Plate\" # Remove batch effect for plotting. ) plots <- SplineOmics::explore_data( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"run-limma-spline-analysis","dir":"Articles","previous_headings":"","what":"Run limma spline analysis","title":"RNA-seq analysis","text":"example, skipping finding best hyperparameters screen_limma_hyperparams() function, already clear idea use. can customize normalization method providing specific normalization function normalize_func argument preprocess_rna_seq_data() function. details use feature, please refer function documentation available βReferencesβ website. Additionally, use preprocess_rna_seq_data() optional RNA-seq data. Alternatively, can use limma::voom function directly pass resulting voom object rna_seq_data argument create_splineomics() update_splineomics(). Alongside , must pass $E data matrix data argument. general, long data argument contains actual data matrix rna_seq_data argument contains object compatible limma, data correctly processed. Run run_limma_splines() function updated SplineOmics object: output function run_limma_splines() named list, element specific βcategoryβ results. Refer document explanation different result categories. elements list, containing elements respective limma topTables, either level comparison two levels. element βtime_effectβ list, element topTable p-value feature respective level reported. element βavrg_diff_conditionsβ list contains elements topTables, represent comparison average differences levels. element βinteraction_condition_timeβ list contains elements topTables, represent interaction levels (includes time average differences)","code":"spline_params = list( spline_type = c(\"n\"), # natural cubic splines dof = c(3L) # Degree of freedom of 2 for the splines. ) voom_obj <- preprocess_rna_seq_data( raw_counts = data, meta = meta, spline_params = spline_params, design = \"~ 1 + Condition*X + Plate\" ) #> Preprocessing RNA-seq data (normalization + voom)... data <- voom_obj$E splineomics <- SplineOmics::update_splineomics( splineomics = splineomics, data = data, rna_seq_data = voom_obj, design = \"~ 1 + Condition*X + Plate\", mode = \"integrated\", # means limma uses the full data for each condition. spline_params = spline_params ) splineomics <- SplineOmics::run_limma_splines( splineomics = splineomics ) #> Hint: The data contains negative values. This may occur if the data has been transformed (e.g., log-transformed or normalized) and is valid in such cases. Ensure that the data preprocessing aligns with your analysis requirements. #> Column 'Plate' of meta will be used to remove the batch effect for the plotting #> Make sure that the design formula contains no interaction between the condition and time (X) for mode == isolated, and that it contains an interaction for mode == integrated. Otherwise, you will get an uncaught error of 'coefficients not estimable' or 'subscript out of bounds'. #> Info limma spline analysis completed successfully"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"build-limma-report","dir":"Articles","previous_headings":"","what":"Build limma report","title":"RNA-seq analysis","text":"topTables three limma result categories can used generate p-value histograms volcano plots. can view generated analysis report create_limma_report function .","code":"report_dir <- here::here( \"results\", \"limma_reports\" ) plots <- SplineOmics::create_limma_report( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"cluster-the-hits-significant-features","dir":"Articles","previous_headings":"","what":"Cluster the hits (significant features)","title":"RNA-seq analysis","text":"obtained limma spline results, can cluster hits based temporal pattern (spline shape). define hit setting adj. p-value threshold every level. Hits features (genes ) adj. p-value threshold. Hierarchical clustering used place every hit one many clusters specified specific level. Note dataset, vast amount hits. useful thousands individual plots, also takes long time compute resulting HTML large size, want limit hits plotted. several options: Use low adjusted p-value: approach filters significant features (genes) proceeding analysis visualization. Access customize data: Modify dataframes inside SplineOmics object removing selected set features (genes) plotting. Optimize clustering without generating report: Set report argument cluster_hits() function FALSE (default TRUE). skips generation HTML report, significantly speeding computation omitting creation export plots. can view generated analysis report cluster_hits function . discussed , three limma result categories. cluster_hits() report shows results three, present (category 2 3 can generated design formula contains interaction effect).","code":"# Note: The low adj. p-values are to have less results, so that the HTML report # is smaller in file size. adj_pthresholds <- c( # 0.05 for both levels 0.0000001, # constant (temperature) 0.00001 0.0000001 # temp_shift ) clusters <- c( 4L, # 4 clusters for constant 4L # 4 clusters for temp_shift ) report_dir <- here::here( \"results\", \"clustering_reports\" ) # Note: The treatment_labels and treatment_timepoints variable can be defined # much simpler for this experiment, because all plots should have the same # treatment line (it can be done like shown by the commented out arguments of # plot_info below). The reason why this is done in the explicit way here is to # show how it is possible to potentially have different treatment lines for the # \"double spline\" plots (the ones that compare limma result category 2 and 3). # constant_temp_shift is the first condition of the double spline plot and the # second condition separated by an underscore (it must be exactly like this, # otherwise it does not work. The first condition is always the one that occurs # first from the top in the respective meta column). treatment_labels = list( constant = \"temp shift\", temp_shift = \"temp shift\", constant_temp_shift = \"temp shift\" ) treatment_timepoints = list( constant = 146, temp_shift = 146, constant_temp_shift = 146 ) plot_info <- list( # For the spline plots y_axis_label = \"log2 intensity\", time_unit = \"min\", # our measurements were in minutes # treatment_labels = list(\"temp shift\"), # add this for all conditions # treatment_timepoints = list(146) # temp shift was at 146 hours. treatment_labels = treatment_labels, treatment_timepoints = treatment_timepoints ) genes <- rownames(data) plot_options <- list( # When meta_replicate_column is not there, all datapoints are blue. meta_replicate_column = \"Reactor\" # Colors the data points based on Reactor ) clustering_results <- SplineOmics::cluster_hits( splineomics = splineomics, adj_pthresholds = adj_pthresholds, clusters = clusters, genes = genes, plot_info = plot_info, plot_options = plot_options, report_dir = report_dir, adj_pthresh_avrg_diff_conditions = 0.0000001, adj_pthresh_interaction_condition_time = 0.0000001 )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"perform-gene-set-enrichment-analysis-gsea","dir":"Articles","previous_headings":"","what":"Perform gene set enrichment analysis (GSEA)","title":"RNA-seq analysis","text":"Usually, final step bioinformatics analysis GSEA. clustered hit, respective gene can assigned GSEA performed. , Enrichr databases choice downloaded: Per default file placed current working directory, root dir R project. run GSEA, downloaded database file loaded dataframe. , optionally, clusterProfiler parameters report dir can specified. function create_gsea_report() runs GSEA using clusterProfiler, generates HTML report returns GSEA dotplots R. function runs clusterProfiler clusters levels, generates HTML report: can view generated analysis report run_gsea function .","code":"# Specify which databases you want to download from Enrichr gene_set_lib <- c( \"WikiPathways_2019_Human\", \"NCI-Nature_2016\", \"TRRUST_Transcription_Factors_2019\", \"MSigDB_Hallmark_2020\", \"GO_Cellular_Component_2018\", \"CORUM\", \"KEGG_2019_Human\", \"TRANSFAC_and_JASPAR_PWMs\", \"ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\", \"GO_Biological_Process_2018\", \"GO_Molecular_Function_2018\", \"Human_Gene_Atlas\" ) SplineOmics::download_enrichr_databases( gene_set_lib = gene_set_lib, output_dir = here::here(), # output into the current working dir (default) filename = \"databases.tsv\" # just the name of the file, not the full path ) # Specify the filepath of the TSV file with the database info downloaded_dbs_filepath <- here::here( \"databases.tsv\" ) # Load the file databases <- read.delim( downloaded_dbs_filepath, sep = \"\\t\", stringsAsFactors = FALSE ) # Specify the clusterProfiler parameters clusterProfiler_params <- list( pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2 ) report_dir <- here::here( \"results\", \"gsea_reports\" ) result <- SplineOmics::run_gsea( # A dataframe with three columns: feature, cluster, and gene. Feature contains # the integer index of the feature, cluster the integer specifying the cluster # number, and gene the string of the gene, such as \"CLSTN2\". levels_clustered_hits = clustering_results$clustered_hits_levels, databases = databases, clusterProfiler_params = clusterProfiler_params, report_info = report_info, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"RNA-seq analysis","text":"","code":"#> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Europe/Vienna #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices datasets utils methods base #> #> other attached packages: #> [1] knitr_1.48 dplyr_1.1.4 here_1.0.1 readr_2.1.5 #> [5] SplineOmics_0.1.0 #> #> loaded via a namespace (and not attached): #> [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.9 #> [4] shape_1.4.6.1 magrittr_2.0.3 farver_2.1.2 #> [7] nloptr_2.1.1 rmarkdown_2.28 GlobalOptions_0.1.2 #> [10] fs_1.6.4 ragg_1.3.3 vctrs_0.6.5 #> [13] minqa_1.2.8 base64enc_0.1-3 htmltools_0.5.8.1 #> [16] progress_1.2.3 broom_1.0.7 variancePartition_1.32.5 #> [19] sass_0.4.9 KernSmooth_2.23-22 bslib_0.8.0 #> [22] htmlwidgets_1.6.4 desc_1.4.3 pbkrtest_0.5.3 #> [25] plyr_1.8.9 cachem_1.1.0 lifecycle_1.0.4 #> [28] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.6-5 #> [31] R6_2.5.1 fastmap_1.2.0 rbibutils_2.3 #> [34] clue_0.3-65 digest_0.6.37 numDeriv_2016.8-1.1 #> [37] colorspace_2.1-1 patchwork_1.3.0 S4Vectors_0.40.2 #> [40] rprojroot_2.0.4 textshaping_0.4.0 fansi_1.0.6 #> [43] compiler_4.3.3 withr_3.0.1 bit64_4.5.2 #> [46] aod_1.3.3 doParallel_1.0.17 backports_1.5.0 #> [49] BiocParallel_1.36.0 viridis_0.6.5 dendextend_1.18.0 #> [52] gplots_3.2.0 MASS_7.3-60.0.1 rjson_0.2.23 #> [55] corpcor_1.6.10 gtools_3.9.5 caTools_1.18.3 #> [58] tools_4.3.3 zip_2.3.1 remaCor_0.0.18 #> [61] glue_1.8.0 nlme_3.1-164 grid_4.3.3 #> [64] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3 #> [67] gtable_0.3.5 tzdb_0.4.0 tidyr_1.3.1 #> [70] hms_1.1.3 utf8_1.2.4 BiocGenerics_0.48.1 #> [73] ggrepel_0.9.6 foreach_1.5.2 pillar_1.9.0 #> [76] stringr_1.5.1 vroom_1.6.5 limma_3.58.1 #> [79] circlize_0.4.16 splines_4.3.3 lattice_0.22-5 #> [82] renv_1.0.10 bit_4.5.0 tidyselect_1.2.1 #> [85] locfit_1.5-9.10 ComplexHeatmap_2.18.0 gridExtra_2.3 #> [88] IRanges_2.36.0 edgeR_4.0.16 svglite_2.1.3 #> [91] RhpcBLASctl_0.23-42 stats4_4.3.3 xfun_0.48 #> [94] Biobase_2.62.0 statmod_1.5.0 matrixStats_1.4.1 #> [97] pheatmap_1.0.12 stringi_1.8.4 yaml_2.3.10 #> [100] boot_1.3-29 evaluate_1.0.0 codetools_0.2-19 #> [103] tibble_3.2.1 BiocManager_1.30.25 cli_3.6.3 #> [106] systemfonts_1.1.0 Rdpack_2.6.1 munsell_0.5.1 #> [109] jquerylib_0.1.4 Rcpp_1.0.13 EnvStats_3.0.0 #> [112] png_0.1-8 parallel_4.3.3 pkgdown_2.1.1 #> [115] ggplot2_3.5.1 prettyunits_1.2.0 bitops_1.0-9 #> [118] lme4_1.1-35.5 viridisLite_0.4.2 mvtnorm_1.3-1 #> [121] lmerTest_3.1-3 scales_1.3.0 openxlsx_4.2.7.1 #> [124] purrr_1.0.2 crayon_1.5.3 fANCOVA_0.6-1 #> [127] GetoptLong_1.0.5 rlang_1.1.4"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"about-this-vignette","dir":"Articles","previous_headings":"","what":"About this vignette","title":"RNA_seq_analysis","text":"tutorial intends showcase explain capabilities SplineOmics package walking real complete RNA-seq example, start finish. SplineOmics explained detail get-started vignette, proteomics example covered. vignette focused showing RNA-seq data can used, , less details overall package provided .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"data-overview","dir":"Articles","previous_headings":"About this vignette","what":"Data Overview","title":"RNA_seq_analysis","text":"dataset originates time-series RNA-seq experiment designed study Chinese Hamster Ovary (CHO) cells. experiment involved cultivating cells eight bioreactors, four bioreactors subjected temperature shift 146 hours (experimental condition) remaining four bioreactors maintained without temperature shift (control condition). Samples collected 17 distinct time points throughout experiment, specifically: \"72h\", \"76h\", \"96h\", \"120h\", \"124h\", \"144h\", \"148h\", \"152h\", \"168h\", \"192h\", \"216h\", \"220h\", \"240h\", \"264h\", \"268h\", \"288h\", \"312h\" cultivation start. time point sampled eight bioreactors, resulting total 136 samples. two batch effects: Reactor Plate. Different bioreactors, cells cultivated, can show big variablity. Plate refers two different plates used RNA-seq analysis samples. fully random design used distribute samples two plates. vignette, just remove plate batch effect. data matrix comprises genes rows samples columns, providing gene expression measurements time points. sample initially sequenced three technical replicates across two NovaSeq X flow cells. technical replicates collapsed generate final dataset used analysis. goal experiment investigate effect temperature shift CHO cell cultivation gene expression dynamics time. Note: original dataset, yet published time vignetteβs creation. demonstration purposes, genes randomly shuffled, subset data included reduce dataset size.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"analysis-goals","dir":"Articles","previous_headings":"About this vignette","what":"Analysis Goals","title":"RNA_seq_analysis","text":"main objectives analysis : Identify genes significant temporal changes: Among thousands genes measured, goal identify exhibit significant changes expression time. Cluster genes based temporal patterns: Genes showing significant temporal changes (hits) grouped clusters based time-dependent expression patterns. Perform gene set enrichment analysis: cluster, gene set enrichment analysis conducted identify whether specific biological pathways processes - downregulated response feeding processes influenced temperature shift. Assess impact temperature shifts temporal patterns: analysis determine whether temporal patterns gene expression affected temperature shift, .e., whether gene expression dynamics differ time temperature shift conditions compared controls.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"load-the-packages","dir":"Articles","previous_headings":"","what":"Load the packages","title":"RNA_seq_analysis","text":"","code":"library(SplineOmics) #> Warning: replacing previous import 'limma::topTable' by #> 'variancePartition::topTable' when loading 'SplineOmics' library(readr) # For reading the meta CSV file library(here) # For managing filepaths #> here() starts at /home/thomas/Documents/PhD/projects/DGTX/R_packages/SplineOmics library(dplyr) # For data manipulation #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(knitr) # For Showing the head of the data and the meta tables."},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"load-the-files","dir":"Articles","previous_headings":"","what":"Load the files","title":"RNA_seq_analysis","text":"","code":"data <- readRDS(xzfile(system.file( \"extdata\", \"rna_seq_data.rds.xz\", package = \"SplineOmics\" ))) meta <- readr::read_csv( system.file( \"extdata\", \"rna_seq_meta.csv\", package = \"SplineOmics\" ), show_col_types = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"show-top-rows-of-data","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of data","title":"RNA_seq_analysis","text":"data: numeric matrix row represents gene (features) column corresponds sample. row names matrix contain gene identifiers, columns aligned sample metadata meta. matrix contains expression values 136 samples.","code":"kable( head(data), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"show-top-rows-of-meta","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of meta","title":"RNA_seq_analysis","text":"meta: data frame containing metadata information samples data. row meta corresponds column data, ensuring 1:1 alignment metadata entries expression data samples. columns meta describe various attributes samples, SampleNr, Reactor, Time, Condition, Plate.","code":"kable( head(meta), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"preprocess-the-data","dir":"Articles","previous_headings":"","what":"Preprocess the data","title":"RNA_seq_analysis","text":"Filter data rows (genes) zero counts across samples. step standard preprocessing procedure RNA-seq data analysis, genes zero counts samples provide information downstream analyses.","code":"rows_before <- nrow(data) # Filter data rows data <- data[rowSums(data) > 0, ] rows_after <- nrow(data) rows_removed <- rows_before - rows_after cat(sprintf( \"Rows before filtering: %d\\nRows after filtering: %d\\nRows removed: %d\\n\", rows_before, rows_after, rows_removed )) #> Rows before filtering: 1000 #> Rows after filtering: 944 #> Rows removed: 56"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"perform-eda-exploratory-data-analysis","dir":"Articles","previous_headings":"","what":"Perform EDA (exploratory data analysis)","title":"RNA_seq_analysis","text":"can see HTML report explore_data() function batch-corrected data, report batch-corrected data.","code":"report_info <- list( omics_data_type = \"RNA\", data_description = \"RNA-seq data of CHO cells\", data_collection_date = \"December 2024\", analyst_name = \"Thomas Rauter\", contact_info = \"thomas.rauter@plus.ac.at\", project_name = \"DGTX\" ) report_dir <- here::here( \"results\", \"explore_data\" ) splineomics <- SplineOmics::create_splineomics( data = data, meta = meta, report_info = report_info, condition = \"Condition\", # Column of meta that contains the levels. meta_batch_column = \"Plate\" # Remove batch effect for plotting. ) plots <- SplineOmics::explore_data( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"run-limma-spline-analysis","dir":"Articles","previous_headings":"","what":"Run limma spline analysis","title":"RNA_seq_analysis","text":"example, skipping finding best hyperparameters screen_limma_hyperparams() function, already clear idea use. can customize normalization method providing specific normalization function normalize_func argument preprocess_rna_seq_data() function. details use feature, please refer function documentation available βReferencesβ website. Additionally, use preprocess_rna_seq_data() optional RNA-seq data. Alternatively, can use limma::voom function directly pass resulting voom object rna_seq_data argument create_splineomics() update_splineomics(). Alongside , must pass $E data matrix data argument. general, long data argument contains actual data matrix rna_seq_data argument contains object compatible limma, data correctly processed. Run run_limma_splines() function updated SplineOmics object: output function run_limma_splines() named list, element specific βcategoryβ results. Refer document explanation different result categories. elements list, containing elements respective limma topTables, either level comparison two levels. element βtime_effectβ list, element topTable p-value feature respective level reported. element βavrg_diff_conditionsβ list contains elements topTables, represent comparison average differences levels. element βinteraction_condition_timeβ list contains elements topTables, represent interaction levels (includes time average differences)","code":"spline_params = list( spline_type = c(\"n\"), # natural cubic splines dof = c(3L) # Degree of freedom of 2 for the splines. ) voom_obj <- preprocess_rna_seq_data( raw_counts = data, meta = meta, spline_params = spline_params, design = \"~ 1 + Condition*X + Plate\" ) #> Preprocessing RNA-seq data (normalization + voom)... data <- voom_obj$E splineomics <- SplineOmics::update_splineomics( splineomics = splineomics, data = data, rna_seq_data = voom_obj, design = \"~ 1 + Condition*X + Plate\", mode = \"integrated\", # means limma uses the full data for each condition. spline_params = spline_params ) splineomics <- SplineOmics::run_limma_splines( splineomics = splineomics ) #> Hint: The data contains negative values. This may occur if the data has been transformed (e.g., log-transformed or normalized) and is valid in such cases. Ensure that the data preprocessing aligns with your analysis requirements. #> Column 'Plate' of meta will be used to remove the batch effect for the plotting #> Make sure that the design formula contains no interaction between the condition and time (X) for mode == isolated, and that it contains an interaction for mode == integrated. Otherwise, you will get an uncaught error of 'coefficients not estimable' or 'subscript out of bounds'. #> Info limma spline analysis completed successfully"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"build-limma-report","dir":"Articles","previous_headings":"","what":"Build limma report","title":"RNA_seq_analysis","text":"topTables three limma result categories can used generate p-value histograms volcano plots. can view generated analysis report create_limma_report function .","code":"report_dir <- here::here( \"results\", \"limma_reports\" ) plots <- SplineOmics::create_limma_report( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"cluster-the-hits-significant-features","dir":"Articles","previous_headings":"","what":"Cluster the hits (significant features)","title":"RNA_seq_analysis","text":"obtained limma spline results, can cluster hits based temporal pattern (spline shape). define hit setting adj. p-value threshold every level. Hits features (genes ) adj. p-value threshold. Hierarchical clustering used place every hit one many clusters specified specific level. Note dataset, vast amount hits. useful thousands individual plots, also takes long time compute resulting HTML large size, want limit hits plotted. several options: Use low adjusted p-value: approach filters significant features (genes) proceeding analysis visualization. Access customize data: Modify dataframes inside SplineOmics object removing selected set features (genes) plotting. Optimize clustering without generating report: Set report argument cluster_hits() function FALSE (default TRUE). skips generation HTML report, significantly speeding computation omitting creation export plots. can view generated analysis report cluster_hits function . discussed , three limma result categories. cluster_hits() report shows results three, present (category 2 3 can generated design formula contains interaction effect).","code":"# Note: The low adj. p-values are to have less results, so that the HTML report # is smaller in file size. adj_pthresholds <- c( # 0.05 for both levels 0.0000001, # constant (temperature) 0.00001 0.0000001 # temp_shift ) clusters <- c( 4L, # 4 clusters for constant 4L # 4 clusters for temp_shift ) report_dir <- here::here( \"results\", \"clustering_reports\" ) # Note: The treatment_labels and treatment_timepoints variable can be defined # much simpler for this experiment, because all plots should have the same # treatment line (it can be done like shown by the commented out arguments of # plot_info below). The reason why this is done in the explicit way here is to # show how it is possible to potentially have different treatment lines for the # \"double spline\" plots (the ones that compare limma result category 2 and 3). # constant_temp_shift is the first condition of the double spline plot and the # second condition separated by an underscore (it must be exactly like this, # otherwise it does not work. The first condition is always the one that occurs # first from the top in the respective meta column). treatment_labels = list( constant = \"temp shift\", temp_shift = \"temp shift\", constant_temp_shift = \"temp shift\" ) treatment_timepoints = list( constant = 146, temp_shift = 146, constant_temp_shift = 146 ) plot_info <- list( # For the spline plots y_axis_label = \"log2 intensity\", time_unit = \"min\", # our measurements were in minutes # treatment_labels = list(\"temp shift\"), # add this for all conditions # treatment_timepoints = list(146) # temp shift was at 146 hours. treatment_labels = treatment_labels, treatment_timepoints = treatment_timepoints ) genes <- rownames(data) plot_options <- list( # When meta_replicate_column is not there, all datapoints are blue. meta_replicate_column = \"Reactor\" # Colors the data points based on Reactor ) clustering_results <- SplineOmics::cluster_hits( splineomics = splineomics, adj_pthresholds = adj_pthresholds, clusters = clusters, genes = genes, plot_info = plot_info, plot_options = plot_options, report_dir = report_dir, adj_pthresh_avrg_diff_conditions = 0.0000001, adj_pthresh_interaction_condition_time = 0.0000001 )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"perform-gene-set-enrichment-analysis-gsea","dir":"Articles","previous_headings":"","what":"Perform gene set enrichment analysis (GSEA)","title":"RNA_seq_analysis","text":"Usually, final step bioinformatics analysis GSEA. clustered hit, respective gene can assigned GSEA performed. , Enrichr databases choice downloaded: Per default file placed current working directory, root dir R project. run GSEA, downloaded database file loaded dataframe. , optionally, clusterProfiler parameters report dir can specified. function create_gsea_report() runs GSEA using clusterProfiler, generates HTML report returns GSEA dotplots R. function runs clusterProfiler clusters levels, generates HTML report: can view generated analysis report run_gsea function .","code":"# Specify which databases you want to download from Enrichr gene_set_lib <- c( \"WikiPathways_2019_Human\", \"NCI-Nature_2016\", \"TRRUST_Transcription_Factors_2019\", \"MSigDB_Hallmark_2020\", \"GO_Cellular_Component_2018\", \"CORUM\", \"KEGG_2019_Human\", \"TRANSFAC_and_JASPAR_PWMs\", \"ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\", \"GO_Biological_Process_2018\", \"GO_Molecular_Function_2018\", \"Human_Gene_Atlas\" ) SplineOmics::download_enrichr_databases( gene_set_lib = gene_set_lib, output_dir = here::here(), # output into the current working dir (default) filename = \"databases.tsv\" # just the name of the file, not the full path ) # Specify the filepath of the TSV file with the database info downloaded_dbs_filepath <- here::here( \"databases.tsv\" ) # Load the file databases <- read.delim( downloaded_dbs_filepath, sep = \"\\t\", stringsAsFactors = FALSE ) # Specify the clusterProfiler parameters clusterProfiler_params <- list( pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2 ) report_dir <- here::here( \"results\", \"gsea_reports\" ) result <- SplineOmics::run_gsea( # A dataframe with three columns: feature, cluster, and gene. Feature contains # the integer index of the feature, cluster the integer specifying the cluster # number, and gene the string of the gene, such as \"CLSTN2\". levels_clustered_hits = clustering_results$clustered_hits_levels, databases = databases, clusterProfiler_params = clusterProfiler_params, report_info = report_info, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"RNA_seq_analysis","text":"","code":"#> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Europe/Vienna #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices datasets utils methods base #> #> other attached packages: #> [1] knitr_1.48 dplyr_1.1.4 here_1.0.1 readr_2.1.5 #> [5] SplineOmics_0.1.0 #> #> loaded via a namespace (and not attached): #> [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.9 #> [4] shape_1.4.6.1 magrittr_2.0.3 farver_2.1.2 #> [7] nloptr_2.1.1 rmarkdown_2.28 GlobalOptions_0.1.2 #> [10] fs_1.6.4 ragg_1.3.3 vctrs_0.6.5 #> [13] minqa_1.2.8 base64enc_0.1-3 htmltools_0.5.8.1 #> [16] progress_1.2.3 broom_1.0.7 variancePartition_1.32.5 #> [19] sass_0.4.9 KernSmooth_2.23-22 bslib_0.8.0 #> [22] htmlwidgets_1.6.4 desc_1.4.3 pbkrtest_0.5.3 #> [25] plyr_1.8.9 cachem_1.1.0 lifecycle_1.0.4 #> [28] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.6-5 #> [31] R6_2.5.1 fastmap_1.2.0 rbibutils_2.3 #> [34] clue_0.3-65 digest_0.6.37 numDeriv_2016.8-1.1 #> [37] colorspace_2.1-1 patchwork_1.3.0 S4Vectors_0.40.2 #> [40] rprojroot_2.0.4 textshaping_0.4.0 fansi_1.0.6 #> [43] compiler_4.3.3 withr_3.0.1 bit64_4.5.2 #> [46] aod_1.3.3 doParallel_1.0.17 backports_1.5.0 #> [49] BiocParallel_1.36.0 viridis_0.6.5 dendextend_1.18.0 #> [52] gplots_3.2.0 MASS_7.3-60.0.1 rjson_0.2.23 #> [55] corpcor_1.6.10 gtools_3.9.5 caTools_1.18.3 #> [58] tools_4.3.3 zip_2.3.1 remaCor_0.0.18 #> [61] glue_1.8.0 nlme_3.1-164 grid_4.3.3 #> [64] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3 #> [67] gtable_0.3.5 tzdb_0.4.0 tidyr_1.3.1 #> [70] hms_1.1.3 utf8_1.2.4 BiocGenerics_0.48.1 #> [73] ggrepel_0.9.6 foreach_1.5.2 pillar_1.9.0 #> [76] stringr_1.5.1 vroom_1.6.5 limma_3.58.1 #> [79] circlize_0.4.16 splines_4.3.3 lattice_0.22-5 #> [82] renv_1.0.10 bit_4.5.0 tidyselect_1.2.1 #> [85] locfit_1.5-9.10 ComplexHeatmap_2.18.0 gridExtra_2.3 #> [88] IRanges_2.36.0 edgeR_4.0.16 svglite_2.1.3 #> [91] RhpcBLASctl_0.23-42 stats4_4.3.3 xfun_0.48 #> [94] Biobase_2.62.0 statmod_1.5.0 matrixStats_1.4.1 #> [97] pheatmap_1.0.12 stringi_1.8.4 yaml_2.3.10 #> [100] boot_1.3-29 evaluate_1.0.0 codetools_0.2-19 #> [103] tibble_3.2.1 BiocManager_1.30.25 cli_3.6.3 #> [106] systemfonts_1.1.0 Rdpack_2.6.1 munsell_0.5.1 #> [109] jquerylib_0.1.4 Rcpp_1.0.13 EnvStats_3.0.0 #> [112] png_0.1-8 parallel_4.3.3 pkgdown_2.1.1 #> [115] ggplot2_3.5.1 prettyunits_1.2.0 bitops_1.0-9 #> [118] lme4_1.1-35.5 viridisLite_0.4.2 mvtnorm_1.3-1 #> [121] lmerTest_3.1-3 scales_1.3.0 openxlsx_4.2.7.1 #> [124] purrr_1.0.2 crayon_1.5.3 fANCOVA_0.6-1 #> [127] GetoptLong_1.0.5 rlang_1.1.4"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Designing a Limma Design Formula","text":"limma package powerful tool analyzing gene expression data, particularly context microarrays RNA-seq. critical part limma analysis design formula, specifies experimental conditions contrasts interested . vignette provides guide construct limma design formula correctly, examples best practices.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"understanding-the-design-matrix","dir":"Articles","previous_headings":"","what":"Understanding the Design Matrix","title":"Designing a Limma Design Formula","text":"design matrix crucial component differential expression analysis using limma. defines relationships samples experimental conditions (factors) investigation. well-constructed design matrix allows limma correctly model effects factors estimate differential expression.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"basic-design-formula","dir":"Articles","previous_headings":"Understanding the Design Matrix","what":"Basic Design Formula","title":"Designing a Limma Design Formula","text":"simplest form, design formula includes one factor, treatment vs.Β control. experiment involves comparing two conditions (e.g., treated vs.Β untreated), can create design formula like : , condition factor variable metadata (meta) represents experimental groups. Important Points: ~ 0 + condition syntax tells R create design matrix without intercept (.e., matrix level factor condition represented column). approach helpful want make direct comparisons conditions. Including Multiple Factors experiment includes one factor, time points treatments, can include design formula: formula assumes effects treatment time additive (interaction). suspect interaction treatment time might important, can include interaction term: Interaction Term: treatment * time term includes main effects treatment time interaction. Blocking Factors experiments, might technical biological replicates, blocking factors (e.g., batch effects). can include blocking factors design formula: formula accounts treatment batch effects, ensuring analysis confounded batch effects.","code":"design <- model.matrix(~ 0 + condition, data = meta) design <- model.matrix(~ 0 + treatment + time, data = meta) design <- model.matrix(~ 0 + treatment * time, data = meta) design <- model.matrix(~ 0 + treatment + batch, data = meta)"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"creating-contrasts","dir":"Articles","previous_headings":"","what":"Creating Contrasts","title":"Designing a Limma Design Formula","text":"defining design matrix, likely want make specific comparisons conditions. contrasts come . example, compare treated vs.Β untreated, can define contrast matrix:","code":"contrast <- makeContrasts( treated_vs_untreated = treatmenttreated - treatmentuntreated, levels = design )"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"practical-example","dir":"Articles","previous_headings":"","what":"Practical Example","title":"Designing a Limma Design Formula","text":"Letβs say experiment two treatments (B) two time points (early late). metadata might look like : design formula : contrast compare treatment early time point treatment B late time point :","code":"meta <- data.frame( sample = c(\"S1\", \"S2\", \"S3\", \"S4\"), treatment = factor(c(\"A\", \"A\", \"B\", \"B\")), time = factor(c(\"early\", \"late\", \"early\", \"late\")) ) design <- model.matrix(~ 0 + treatment * time, data = meta) contrast <- makeContrasts( A_early_vs_B_late = (treatmentA:timeearly) - (treatmentB:timelate), levels = design )"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Designing a Limma Design Formula","text":"Starting ~ 0 means intercept (.e., including baseline group model). Starting ~ 1 (just ~) includes intercept (baseline group). Factors separated + indicate additive effects. example, ~ 0 + factor1 + factor2 means modeling effects factor1 factor2 additively, without considering interactions. * symbol used model interactions factors. example, ~ 0 + factor1 * factor2 include factor1, factor2, interaction (factor1:factor2). Alternatively, can specify interaction explicitly :. example, ~ 0 + factor1 + factor2 + factor1:factor2 equivalent ~ 0 + factor1 * factor2.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"some-examples","dir":"Articles","previous_headings":"Summary","what":"Some examples:","title":"Designing a Limma Design Formula","text":"~ 0 + factor1 + factor2: Additive model without intercept. ~ 1 + factor1 + factor2: Additive model intercept. ~ 0 + factor1 * factor2: Model main effects interaction, intercept. ~ 1 + factor1 * factor2: Model intercept, main effects, interaction.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"Designing a Limma Design Formula","text":"","code":"## R version 4.3.3 (2024-02-29) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 22.04.5 LTS ## ## Matrix products: default ## BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 ## ## locale: ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ## [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 ## [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 ## [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C ## ## time zone: Europe/Vienna ## tzcode source: system (glibc) ## ## attached base packages: ## [1] stats graphics grDevices datasets utils methods base ## ## loaded via a namespace (and not attached): ## [1] digest_0.6.37 desc_1.4.3 R6_2.5.1 ## [4] fastmap_1.2.0 xfun_0.48 cachem_1.1.0 ## [7] knitr_1.48 htmltools_0.5.8.1 rmarkdown_2.28 ## [10] lifecycle_1.0.4 cli_3.6.3 sass_0.4.9 ## [13] pkgdown_2.1.1 textshaping_0.4.0 jquerylib_0.1.4 ## [16] renv_1.0.10 systemfonts_1.1.0 compiler_4.3.3 ## [19] rstudioapi_0.16.0 tools_4.3.3 ragg_1.3.3 ## [22] bslib_0.8.0 evaluate_1.0.0 yaml_2.3.10 ## [25] BiocManager_1.30.25 jsonlite_1.8.9 htmlwidgets_1.6.4 ## [28] rlang_1.1.4 fs_1.6.4"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"about-this-tutorial","dir":"Articles","previous_headings":"","what":"About this tutorial","title":"get-started","text":"tutorial intends showcase explain capabilities SplineOmics package walking real complete example, start finish.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"example-overview","dir":"Articles","previous_headings":"About this tutorial","what":"Example Overview","title":"get-started","text":"example involves time-series proteomics experiment, CHO (chinese hamster ovary) cells cultivated three bioreactors (three biological replicates). experiment includes following setup: Samples taken exponential stationary growth phases. 60 minutes feeding 15, 60, 90, 120, 240 minutes feeding","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"analysis-goals","dir":"Articles","previous_headings":"About this tutorial","what":"Analysis Goals","title":"get-started","text":"main goals analysis : Identify proteins significant temporal changes: 7162 cellular proteins, objective detect proteins show significant change time CHO cells fed (.e., impact feeding). Cluster hits based temporal patterns: proteins (hits) significant temporal changes clustered according time-based patterns. Perform gene set enrichment analysis: cluster, gene set enrichment analysis performed determine specific biological processes - downregulated feeding.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"note","dir":"Articles","previous_headings":"About this tutorial","what":"Note","title":"get-started","text":"documentation SplineOmics package functions can viewed ","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"load-the-packages","dir":"Articles","previous_headings":"","what":"Load the packages","title":"get-started","text":"","code":"library(SplineOmics) #> Warning: replacing previous import 'limma::topTable' by #> 'variancePartition::topTable' when loading 'SplineOmics' library(readxl) # for loading Excel files library(here) # For managing filepaths #> here() starts at /home/thomas/Documents/PhD/projects/DGTX/R_packages/SplineOmics library(dplyr) # For data manipulation #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"load-the-files","dir":"Articles","previous_headings":"","what":"Load the files","title":"get-started","text":"example, proteomics_data.rds file contains numeric values (intensities) also feature descriptions, gene protein name (= annotation part). Usually, load data example Excel file, .rds file compressed, reason format chosen limit size SplineOmics package. file meta.xlsx contains meta information, descriptions columns numeric values data. (example files part package donβt present system). Please note dataset actual experimental dataset, annotation information, gene names, removed since yet published time making SplineOmics package public. Instead, dataset includes randomly generated gene symbols gene names corresponding Cricetulus griseus (Chinese Hamster) row. intended demonstrate functionality package. left part data contains numeric values, right part annotation info, can copied separate dataframe, shown . Three comments characteristics input data : data must contain NA values special values, must consist numbers. example, original proteomics data contained NA values, resolved case imputation (replacing NA values numbers). features data ideally normally distributed analyzed limma, fits linear model feature. models rely statistical tests assume normality. Although limma can still function data normally distributed, resulting p-values may become less reliable. reason, strongly recommended transform data using techniques log2 transformation features deviate normality. Proper transformation helps ensure assumptions underlying statistical tests met, leading accurate trustworthy results. samples data independent . Linear models, used limma, assume observations (samples) independent. dependency samples (e.g., repeated measurements subject), assumption violated, can lead incorrect statistical inferences.","code":"data <- readRDS(xzfile(system.file( \"extdata\", \"proteomics_data.rds.xz\", package = \"SplineOmics\" ))) meta <- read_excel( system.file( \"extdata\", \"proteomics_meta.xlsx\", package = \"SplineOmics\" ) ) # Extract the annotation part from the dataframe. first_na_col <- which(is.na(data[1, ]))[1] annotation <- data |> dplyr::select((first_na_col + 1):ncol(data)) |> dplyr::slice(-c(1:3)) print(head(data)) #> # A tibble: 6 Γ 40 #> `Sample ID` `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` #> #> 1 Reactor E09 E10 E12 E09 E10 E12 E09 E10 E12 E09 E10 #> 2 Time Point TP01 TP01 TP01 TP02 TP02 TP02 TP03 TP03 TP03 TP04 TP04 #> 3 Phase of Feβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ #> 4 NA 15.2β¦ 15.2β¦ 15.3β¦ 15.1β¦ 15.2β¦ 15.0β¦ 15.2β¦ 15.2β¦ 15.2β¦ 15.1β¦ 15.2β¦ #> 5 NA 15.0β¦ 15.1β¦ 15.2β¦ 15.1β¦ 15.1β¦ 15.2β¦ 15.2β¦ 15.3β¦ 15.2β¦ 15.1β¦ 15.1β¦ #> 6 NA 14.5β¦ 14.7β¦ 14.6β¦ 14.5β¦ 14.6β¦ 14.6β¦ 14.5β¦ 14.6β¦ 14.7β¦ 14.5β¦ 14.6β¦ #> # βΉ 28 more variables: `12` , `13` , `14` , `15` , #> # `16` , `17` , `18` , `19` , `20` , `21` , #> # `22` , `23` , `24` , `25` , `26` , `27` , #> # `28` , `29` , `30` , `31` , `32` , `33` , #> # `34` , `35` , `36` , ...38 , Gene_symbol , #> # Gene_name print(meta) #> # A tibble: 36 Γ 5 #> Sample.ID Reactor Time.Point Phase Time #> #> 1 E09_TP01_Exponential E09 TP01 Exponential -60 #> 2 E10_TP01_Exponential E10 TP01 Exponential -60 #> 3 E12_TP01_Exponential E12 TP01 Exponential -60 #> 4 E09_TP02_Exponential E09 TP02 Exponential 15 #> 5 E10_TP02_Exponential E10 TP02 Exponential 15 #> 6 E12_TP02_Exponential E12 TP02 Exponential 15 #> 7 E09_TP03_Exponential E09 TP03 Exponential 60 #> 8 E10_TP03_Exponential E10 TP03 Exponential 60 #> 9 E12_TP03_Exponential E12 TP03 Exponential 60 #> 10 E09_TP04_Exponential E09 TP04 Exponential 90 #> # βΉ 26 more rows print(annotation) #> # A tibble: 4,162 Γ 2 #> Gene_symbol Gene_name #> #> 1 LOC113838844 cone-rod homeobox protein-like #> 2 Wdr83os WD repeat domain 83 opposite strand #> 3 Cubn cubilin #> 4 Dynlt1 dynein light chain Tctex-type 1 #> 5 Ostc oligosaccharyltransferase complex non-catalytic subunit #> 6 Unc5cl unc-5 family C-terminal like #> 7 Cfl1 cofilin 1 #> 8 LOC100752202 HEN methyltransferase 1 #> 9 LOC100755162 acyl-coenzyme A synthetase ACSM5, mitochondrial #> 10 LOC100768921 40S ribosomal protein S21 #> # βΉ 4,152 more rows"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"bring-the-inputs-into-the-standardized-format","dir":"Articles","previous_headings":"Load the files","what":"Bring the Inputs into the Standardized Format","title":"get-started","text":"Since data format required SplineOmics package, needs processing. SplineOmics package requires data numeric matrix, element allowed anything else number. can done commands R, file specific structure, function extract_data() can handle automatically.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"file-structure-requirements","dir":"Articles","previous_headings":"Load the files > Bring the Inputs into the Standardized Format","what":"File Structure Requirements","title":"get-started","text":"file looks like one used , : data matrix field left annotation info right fields separated one empty column","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"usage-of-the-extract_data-function","dir":"Articles","previous_headings":"Load the files > Bring the Inputs into the Standardized Format","what":"Usage of the extract_data() function","title":"get-started","text":", extract_data() can: Identify data matrix field return numeric matrix. Create column headers information written cells respective columns data matrix field. annotation columns specified, rowheaders increasing numbers. annotation columns specified (like \"First.Protein.Description\" \"ID\" example), combined form rowheaders (feature names).","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"usage-in-plotting","dir":"Articles","previous_headings":"Load the files > Bring the Inputs into the Standardized Format","what":"Usage in Plotting","title":"get-started","text":"generated rowheaders used label plots feature shown individually, : Spline plots datapoints individual feature.","code":"data <- SplineOmics::extract_data( # The dataframe with the numbers on the left and info on the right. data = data, # Use this annotation column for the feature names. feature_name_columns = c(\"Gene_name\"), # When TRUE, you must confirm that data is in the required format. user_prompt = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"perform-eda-exploratory-data-analysis","dir":"Articles","previous_headings":"","what":"Perform EDA (exploratory data analysis)","title":"get-started","text":"Now data required format (numeric matrix) can go . first step analyzing data typically Exploratory Data Analysis (EDA). EDA involves summarizing main characteristics data, often visualizations.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"common-eda-plots","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Common EDA Plots","title":"get-started","text":"common types EDA plots include: Density distributions Boxplots PCA (Principal Component Analysis) Correlation heatmaps , can generate plots lines R code. However, prefer, convenience, explore_data() function can handle .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"using-explore_data-for-eda","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Using explore_data() for EDA","title":"get-started","text":"SplineOmics package provides function explore_data() perform EDA. function requires following arguments: data: numeric data matrix. meta: metadata table. condition: name column metadata contains levels experiment (e.g., βExponentialβ βStationaryβ). report_info: list contains general information analysis, name analyst datatype (e.g. proteomics)","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"optional-arguments","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Optional Arguments","title":"get-started","text":"addition required arguments, explore_data() offers several optional arguments: meta_batch_column: name column contains first batch effect. meta_batch2_column: name column contains second batch effect. least one batch column provided, function : Use removeBatchEffect() function limma remove batch effect data plotting. Generate two EDA HTML reports: one uncorrected data one batch-corrected data.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"output-and-report-options","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Output and Report Options","title":"get-started","text":"default, reports saved current working directory, location can changed using report_dir argument. function also returns plots generated analysis, can modify according needs. want report generated, can set report argument FALSE (example just want figures R environment)","code":"# Those fields are mandatory, because we believe that when such a report is # opened after half a year, those infos can be very helpful. report_info <- list( omics_data_type = \"PTX\", data_description = \"Proteomics data of CHO cells\", data_collection_date = \"February 2024\", analyst_name = \"Thomas Rauter\", contact_info = \"thomas.rauter@plus.ac.at\", project_name = \"DGTX\" ) report_dir <- here::here( \"results\", \"explore_data\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"splineomics-object","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"SplineOmics Object","title":"get-started","text":"SplineOmics package, multiple functions take arguments input. make easier avoid errors, decided arguments provided individually functions, stored R6 object (type βSplineOmicsβ) object passed functions. Additionally, functions generate intermediate output, just necessary next function workflow, also just passed along updating SplineOmics object. donβt worry .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"functionality","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > SplineOmics Object","what":"Functionality","title":"get-started","text":"SplineOmics object can seen container necessary arguments stored. function retrieves required arguments object potentially adds new data results back .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"documentation","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > SplineOmics Object","what":"Documentation","title":"get-started","text":"documentation function creates SplineOmics object can found documentation function updates [documentation function takes SplineOmics object input specifies arguments must present SplineOmics object passed respective function.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"required-arguments-create_splineomics","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Required Arguments create_splineomics()","title":"get-started","text":"data: matrix data meta: Metadata associated data. condition: Meta column name levels (e.g., Exponential Stationary).","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"optional-arguments-create_splineomics","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Optional Arguments create_splineomics()","title":"get-started","text":"rna_seq_data: object containing preprocessed RNA-seq data, output limma::voom function. annotation: dataframe feature descriptions data. report_info: list containing general information analysis. meta_batch_column: Column meta batch information. meta_batch2_column: Column secondary meta batch information. design: limma design formula spline_params: Parameters spline functions. Now SplineOmics object defined, can perform exploratory data analysis. can see HTML report explore_data() function batch-corrected data, report batch-corrected data. EDA plots can tell range things. plots HTML report grouped three categories: Distribution Variability Analysis, Time Series Analysis, Dimensionality Reduction Clustering. look correlation heatmaps HTML report, can see samples E12_TP05_Exponential E10_TP10_Stationary stick . Seeing , might want remove data. can test happens , along testing hyperparameter choices influence results, package function screen_limma_hyperparams().","code":"# splineomics now contains the SplineOmics object. splineomics <- SplineOmics::create_splineomics( data = data, meta = meta, annotation = annotation, report_info = report_info, condition = \"Phase\", # Column of meta that contains the levels. meta_batch_column = \"Reactor\" # For batch effect removal ) # Special print.SplineOmics function leads to selective printing print(splineomics) #> data:SplineOmics Object #> ------------------- #> Number of features (rows): 4162 #> Number of samples (columns): 36 #> Meta data columns: 5 #> First few meta columns: #> # A tibble: 3 Γ 5 #> Sample.ID Reactor Time.Point Phase Time #> #> 1 E09_TP01_Exponential E09 TP01 Exponential -60 #> 2 E10_TP01_Exponential E10 TP01 Exponential -60 #> 3 E12_TP01_Exponential E12 TP01 Exponential -60 #> Condition: Phase #> No RNA-seq data provided. #> Annotation provided with 4162 entries. #> No spline parameters set. #> P-value adjustment method: BH plots <- SplineOmics::explore_data( splineomics = splineomics, # SplineOmics object report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"finding-the-best-hyperparameters","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Finding the Best Hyperparameters","title":"get-started","text":"running limma spline analysis, important find best βhyperparametersβ. context, hyperparameters include: Degree freedom (DoF) Different versions data (e.g., outlier removed vs.Β removed) Different limma design formulas","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"challenge-of-hyperparameter-selection","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Challenge of Hyperparameter Selection","title":"get-started","text":"Rationally determining best combination hyperparameters can challenging. rationally, mean deciding upon final hyperparameters without ever testing , just scientific reasoning. much easier just testing seeing actually behave. However, manually selecting combinations can tedious, work systematically, can challenging. solve problem, screen_limma_hyperparams() function written.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"using-screen_limma_hyperparams","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Using screen_limma_hyperparams()","title":"get-started","text":"function screen_limma_hyperparams() automates process testing different combinations hyperparameters. βs works: Specify values: hyperparameter, can specify values want test. Run combinations: function runs limma spline analysis combinations formed hyperparameters βve provided semi combinatorial way.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"inner-vs--outer-hyperparameters","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Inner vs.Β Outer Hyperparameters","title":"get-started","text":"Semi combinatorial means every possible combination generated. Instead, inner outer hyperparameters: possible combinations outer hyperparameters generated. version data (outer hyperparameter), combinations inner hyperparameters tested. approach neccessary, otherwise amount combos explode.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"example","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Example","title":"get-started","text":"example, two versions dataset (one full dataset, one outliers removed), versions considered outer hyperparameters. Additionaly, lets say, want test two different limma design formulas, formula 1 2. function test combinations outer hyperparameters compare , results total 6 combinations : Full Dataset Formula 1 vs Full Dataset Formula 2 Full Dataset Formula 1 vs Outliers Removed Dataset Formula 1 Full Dataset Formula 1 vs Outliers Removed Dataset Formula 2 Full Dataset Formula 2 vs Outliers Removed Dataset Formula 1 Full Dataset Formula 2 vs Outliers Removed Dataset Formula 2 Outliers Removed Dataset Formula 1 vs Outliers Removed Dataset Formula 2 Letβs say specified following inner hyperparameters: Spline parameters: Natural cubic splines degree freedom either 2 3. Adjusted p-value threshold: 0.05 0.1. function generate test combinations spline parameters p-value thresholds 4 combos: Combo 1: DoF = 2, threshold = 0.05 DoF = 3, threshold = 0.05 DoF = 2, threshold = 0.1 DoF = 3, threshold = 0.1 Combo 2: DoF = 2, threshold = 0.05 DoF = 3, threshold = 0.05 DoF = 2, threshold = 0.1 DoF = 3, threshold = 0.1 Combo 3: β¦ allows systematically explore different combinations select optimal hyperparameters analysis. example proteomics data: Now specified values hyperparameter want test, can run screen_limma_hyperparams() function. mentioned, function generates report comparison outer hyperparameters, many show . can view example report report contains results comparison βouterβ hyperparameters data 1 design (formula) 1 compared data 1 design 2. , combinations βinnerβ hyperparameters generated (every possible combination specified adj. p-value thresholds spline configs). encoding used reports titles (part output screen_limma_hyperparams function).","code":"data1 <- data meta1 <- meta # Remove the \"outliers\" data2 <- data[, !(colnames(data) %in% c( \"E12_TP05_Exponential\", \"E10_TP10_Stationary\" ) )] # Adjust meta so that it matches data2 meta2 <- meta[!meta$Sample.ID %in% c( \"E12_TP05_Exponential\", \"E10_TP10_Stationary\" ), ] # As mentioned above, all the values of one hyperparameter are stored # and provided as a list. datas <- list(data1, data2) # This will be used to describe the versions of the data. datas_descr <- c( \"full_data\", \"outliers_removed\" ) metas <- list(meta1, meta2) # Test two different limma designs designs <- c( \"~ 1 + Phase*X + Reactor\", \"~ 1 + X + Reactor\" ) # 'Integrated means' limma will use the full dataset to generate the results for # each condition. 'Isolated' means limma will use only the respective part of # the dataset for each condition. Designs that contain the condition column # (here Phase) must have mode 'integrated', because the full data is needed to # include the different conditions into the design formula. modes <- c( \"integrated\", \"isolated\" ) # Specify the meta \"level\" column condition <- \"Phase\" report_dir <- here::here( \"results\", \"hyperparams_screen_reports\" ) # To remove the batch effect meta_batch_column <- \"Reactor\" # Test out two different p-value thresholds (inner hyperparameter) adj_pthresholds <- c( 0.05, 0.1 ) # Create a dataframe with combinations of spline parameters to test # (every row a combo to test) spline_test_configs <- data.frame( # 'n' stands for natural cubic splines, b for B-splines. spline_type = c(\"n\", \"n\", \"b\", \"b\"), # Degree is not applicable (NA) for natural splines. degree = c(NA, NA, 2L, 4L), # Degrees of freedom (DoF) to test. # Higher dof means spline can fit more complex patterns. dof = c(2L, 3L, 3L, 4L) ) print(spline_test_configs) #> spline_type degree dof #> 1 n NA 2 #> 2 n NA 3 #> 3 b 2 3 #> 4 b 4 4 SplineOmics::screen_limma_hyperparams( splineomics = splineomics, datas = datas, datas_descr = datas_descr, metas = metas, designs = designs, modes = modes, spline_test_configs = spline_test_configs, report_dir = report_dir, adj_pthresholds = adj_pthresholds, )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"run-limma-spline-analysis","dir":"Articles","previous_headings":"","what":"Run limma spline analysis","title":"get-started","text":"identified hyperparameters likely best ones, can run limma spline analysis get results. Lets just assume now new parameters, SplineOmics object updated, best analysis. choice depends analysis. example, analysis, natural cubic splines (n) dof two seemed fit data best (overfitting, also underfitting), reason spline parameters chosen. design formula, must specify either βisolatedβ βintegratedβ. Isolated means limma determines results level using data level. Integrated means limma determines results levels using full dataset (levels). integrated mode, condition column (Phase) must included design. Isolated means limma uses part dataset belongs level obtain results level. generate limma result categories 2 3 () Run run_limma_splines() function updated SplineOmics object: output function run_limma_splines() named list, element specific βcategoryβ results. Refer document explanation different result categories. elements list, containing elements respective limma topTables, either level comparison two levels. element βtime_effectβ list, element topTable p-value feature respective level reported. element βavrg_diff_conditionsβ list contains elements topTables, represent comparison average differences levels. element βinteraction_condition_timeβ list contains elements topTables, represent interaction levels (includes time average differences)","code":"splineomics <- SplineOmics::update_splineomics( splineomics = splineomics, design = \"~ 1 + Phase*X + Reactor\", # best design formula mode = \"integrated\", # means limma uses the full data for each condition. data = data2, # data without \"outliers\" was better meta = meta2, spline_params = list( spline_type = c(\"n\"), # natural cubic splines (take these if unsure) dof = c(2L) # If you are unsure about which dof, start with 2 and increase ) ) splineomics <- SplineOmics::run_limma_splines( splineomics = splineomics ) #> Column 'Reactor' of meta will be used to remove the batch effect for the plotting #> Make sure that the design formula contains no interaction between the condition and time (X) for mode == isolated, and that it contains an interaction for mode == integrated. Otherwise, you will get an uncaught error of 'coefficients not estimable' or 'subscript out of bounds'. #> Info limma spline analysis completed successfully"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"build-limma-report","dir":"Articles","previous_headings":"","what":"Build limma report","title":"get-started","text":"topTables three limma result categories can used generate p-value histograms volcano plots. can view generated analysis report create_limma_report function . report contains p-value histograms three limma result categories volcano plot category 2. Embedded file downloadable limma topTables results category 1 mode βisolatedβ also results category 2 3 mode βintegratedβ. Note upcoming cluster_hits() function report, embedded file contain clustered significant features result category 1.","code":"report_dir <- here::here( \"results\", \"create_limma_reports\" ) plots <- SplineOmics::create_limma_report( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"cluster-the-hits-significant-features","dir":"Articles","previous_headings":"","what":"Cluster the hits (significant features)","title":"get-started","text":"obtained limma spline results, can cluster hits based temporal pattern (spline shape). define hit setting adj. p-value threshold every level. Hits features (e.g.Β proteins) adj. p-value threshold. Hierarchical clustering used place every hit one many clusters specified specific level. can view generated analysis report cluster_hits function . discussed , three limma result categories. cluster_hits() report shows results three, present (category 2 3 can generated design formula contains interaction effect).","code":"adj_pthresholds <- c( # 0.05 for both levels 0.05, # exponential 0.05 # stationary ) clusters <- c( 6L, # 6 clusters for the exponential phase level 3L # 3 clusters for the stationary phase level ) report_dir <- here::here( \"results\", \"clustering_reports\" ) plot_info <- list( # For the spline plots y_axis_label = \"log2 intensity\", time_unit = \"min\", # our measurements were in minutes treatment_labels = list(\"feeding\"), # add this for all conditions treatment_timepoints = list(0) # Feeding was at 0 minutes. ) # Like this you can add individual treatment labels to your plots: # treatment_labels = list( # exponential = \"treatment 1\", # One treatment in exp # stationary = c(\"treatment 2\", \"treatment 3\"), # Two treatments in stat # additional_condition = NA # No treatment in the hypothetical third condition # ) # # treatment_timepoints = list( # exponential = 0, # stationary = c(100, 140), # Two treatments also need two timepoints # additional_condition = NA # ) # # or set a treatment for ALL conditions (still always make a list): # # treatment_labels = list(\"treatment\") # treatment_timepoints = list(120) # # or set multiple treatments for ALL conditions: # # treatment_labels = list(c(\"treatment1\", \"treatment2\")) # treatment_timepoints = list(c(120, 90)) # Get all the gene names. They are used for generating files # which contents can be directly used as the input for the Enrichr webtool, # if you prefer to manually perform the enrichment. Those files are # embedded in the output HTML report and can be downloaded from there. gene_column_name <- \"Gene_symbol\" genes <- annotation[[gene_column_name]] plot_options <- list( # When meta_replicate_column is not there, all datapoints are blue. meta_replicate_column = \"Reactor\", # Colors the data points based on Reactor cluster_heatmap_columns = FALSE # Per default FALSE, just for demonstration ) clustering_results <- SplineOmics::cluster_hits( splineomics = splineomics, adj_pthresholds = adj_pthresholds, clusters = clusters, genes = genes, plot_info = plot_info, plot_options = plot_options, report_dir = report_dir, adj_pthresh_avrg_diff_conditions = 0, adj_pthresh_interaction_condition_time = 0.25 )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"perform-gene-set-enrichment-analysis-gsea","dir":"Articles","previous_headings":"","what":"Perform gene set enrichment analysis (GSEA)","title":"get-started","text":"Usually, final step bioinformatics analysis GSEA. clustered hit, respective gene can assigned GSEA performed. , Enrichr databases choice downloaded: Per default file placed current working directory, root dir R project. run GSEA, downloaded database file loaded dataframe. , optionally, clusterProfiler parameters report dir can specified. function create_gsea_report() runs GSEA using clusterProfiler, generates HTML report returns GSEA dotplots R. function runs clusterProfiler clusters levels, generates HTML report: can view generated analysis report run_gsea function . report first shows enrichment results, 2 genes supported term, tabular format. table terms < 2 genes supporting can downloaded clicking button table. dotplots , every row term specific database, columns respective clusters. color scale contains info odds ratio size -log10 adj. p-value. terms > 2 genes support included plot. , cluster, just maximally 5 terms shown (terms highest odds ratios). Note example cluster 1 already 5 terms, cluster 2 , gets term also found cluster 1, term included sixth term cluster 1, way maximum 5 can exceeded. phase, like stationary , lead enrichment results, stated red message.","code":"# Specify which databases you want to download from Enrichr gene_set_lib <- c( \"WikiPathways_2019_Human\", \"NCI-Nature_2016\", \"TRRUST_Transcription_Factors_2019\", \"MSigDB_Hallmark_2020\", \"GO_Cellular_Component_2018\", \"CORUM\", \"KEGG_2019_Human\", \"TRANSFAC_and_JASPAR_PWMs\", \"ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\", \"GO_Biological_Process_2018\", \"GO_Molecular_Function_2018\", \"Human_Gene_Atlas\" ) SplineOmics::download_enrichr_databases( gene_set_lib = gene_set_lib, filename = \"databases.tsv\" ) # Specify the filepath of the TSV file with the database info downloaded_dbs_filepath <- here::here(\"databases.tsv\") # Load the file databases <- read.delim( downloaded_dbs_filepath, sep = \"\\t\", stringsAsFactors = FALSE ) # Specify the clusterProfiler parameters clusterProfiler_params <- list( pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2 ) report_dir <- here::here( \"results\", \"gsea_reports\" ) result <- SplineOmics::run_gsea( # A dataframe with three columns: feature, cluster, and gene. Feature contains # the integer index of the feature, cluster the integer specifying the cluster # number, and gene the string of the gene, such as \"CLSTN2\". levels_clustered_hits = clustering_results$clustered_hits_levels, databases = databases, clusterProfiler_params = clusterProfiler_params, report_info = report_info, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"get-started","text":"example showed functionalities SplineOmics package. can also run datatypes , including timeseries RNA-seq glycan data (, refer documentation README file GitHub page Usage/RNA-seq Glycan Data). get interactive version example, download SplineOmics package run function open_tutorial() opens R Markdown file, can run different code blocks working R Studio (recommendet) can easily check values individual variables generate output reports . run function open_template() get minimal R Markdown file, code written can use skeleton plug data run . hope SplineOmics package makes scientific data analysis easier. face problems (bugs code) satisfied documentation, open issue GitHub check options Feedback section README GitHub. Thank !","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"get-started","text":"","code":"#> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Europe/Vienna #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices datasets utils methods base #> #> other attached packages: #> [1] dplyr_1.1.4 here_1.0.1 readxl_1.4.3 SplineOmics_0.1.0 #> #> loaded via a namespace (and not attached): #> [1] bitops_1.0-9 Rdpack_2.6.1 gridExtra_2.3 #> [4] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65 #> [7] GetoptLong_1.0.5 matrixStats_1.4.1 compiler_4.3.3 #> [10] reshape2_1.4.4 png_0.1-8 systemfonts_1.1.0 #> [13] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 #> [16] shape_1.4.6.1 crayon_1.5.3 fastmap_1.2.0 #> [19] backports_1.5.0 caTools_1.18.3 utf8_1.2.4 #> [22] rmarkdown_2.28 nloptr_2.1.1 ragg_1.3.3 #> [25] purrr_1.0.2 xfun_0.48 cachem_1.1.0 #> [28] jsonlite_1.8.9 progress_1.2.3 EnvStats_3.0.0 #> [31] remaCor_0.0.18 BiocParallel_1.36.0 broom_1.0.7 #> [34] parallel_4.3.3 prettyunits_1.2.0 cluster_2.1.6 #> [37] R6_2.5.1 bslib_0.8.0 stringi_1.8.4 #> [40] RColorBrewer_1.1-3 limma_3.58.1 boot_1.3-29 #> [43] cellranger_1.1.0 numDeriv_2016.8-1.1 jquerylib_0.1.4 #> [46] Rcpp_1.0.13 iterators_1.0.14 knitr_1.48 #> [49] base64enc_0.1-3 IRanges_2.36.0 Matrix_1.6-5 #> [52] splines_4.3.3 tidyselect_1.2.1 rstudioapi_0.16.0 #> [55] yaml_2.3.10 viridis_0.6.5 doParallel_1.0.17 #> [58] gplots_3.2.0 codetools_0.2-19 plyr_1.8.9 #> [61] lmerTest_3.1-3 lattice_0.22-5 tibble_3.2.1 #> [64] withr_3.0.1 Biobase_2.62.0 evaluate_1.0.0 #> [67] desc_1.4.3 zip_2.3.1 circlize_0.4.16 #> [70] pillar_1.9.0 BiocManager_1.30.25 KernSmooth_2.23-22 #> [73] renv_1.0.10 foreach_1.5.2 stats4_4.3.3 #> [76] generics_0.1.3 rprojroot_2.0.4 S4Vectors_0.40.2 #> [79] hms_1.1.3 ggplot2_3.5.1 munsell_0.5.1 #> [82] scales_1.3.0 aod_1.3.3 minqa_1.2.8 #> [85] gtools_3.9.5 RhpcBLASctl_0.23-42 glue_1.8.0 #> [88] pheatmap_1.0.12 tools_4.3.3 fANCOVA_0.6-1 #> [91] dendextend_1.18.0 variancePartition_1.32.5 lme4_1.1-35.5 #> [94] openxlsx_4.2.7.1 mvtnorm_1.3-1 fs_1.6.4 #> [97] grid_4.3.3 tidyr_1.3.1 rbibutils_2.3 #> [100] colorspace_2.1-1 nlme_3.1-164 patchwork_1.3.0 #> [103] cli_3.6.3 textshaping_0.4.0 fansi_1.0.6 #> [106] viridisLite_0.4.2 svglite_2.1.3 ComplexHeatmap_2.18.0 #> [109] corpcor_1.6.10 gtable_0.3.5 sass_0.4.9 #> [112] digest_0.6.37 BiocGenerics_0.48.1 pbkrtest_0.5.3 #> [115] ggrepel_0.9.6 rjson_0.2.23 htmlwidgets_1.6.4 #> [118] farver_2.1.2 htmltools_0.5.8.1 pkgdown_2.1.1 #> [121] lifecycle_1.0.4 GlobalOptions_0.1.2 statmod_1.5.0 #> [124] MASS_7.3-60.0.1"},{"path":"https://csbg.github.io/SplineOmics/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Thomas Rauter. Author, maintainer.","code":""},{"path":"https://csbg.github.io/SplineOmics/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Rauter T (2025). SplineOmics: Streamlines process analysing omics timeseries data splines. R package version 0.1.0, https://csbg.github.io/SplineOmics.","code":"@Manual{, title = {SplineOmics: Streamlines the process of analysing omics timeseries data with splines}, author = {Thomas Rauter}, year = {2025}, note = {R package version 0.1.0}, url = {https://csbg.github.io/SplineOmics}, }"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"splineomics","dir":"","previous_headings":"","what":"Streamlines the process of analysing omics timeseries data with splines","title":"Streamlines the process of analysing omics timeseries data with splines","text":"R package SplineOmics finds significant features (hits) time-series -omics data using splines limma hypothesis testing. clusters hits based spline shape showing results summary HTML reports. graphical abstract shows full workflow streamlined SplineOmics: Graphical Abstract SplineOmics Workflow","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of Contents","title":"Streamlines the process of analysing omics timeseries data with splines","text":"π Introduction π³ Docker Container Tutorial Details RNA-seq Glycan Data π¦ Dependencies π Reading β Getting Help π€ Contributing π¬ Feedback π License π Citation π Contributors π Acknowledgements","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-introduction","dir":"","previous_headings":"","what":"π Introduction","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Welcome SplineOmics, R package designed streamline analysis -omics time-series data, followed automated HTML report generation.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"is-the-splineomics-package-of-use-for-me","dir":"","previous_headings":"π Introduction","what":"Is the SplineOmics package of use for me?","title":"Streamlines the process of analysing omics timeseries data with splines","text":"-omics data time, package help run limma splines, decide parameters use, perform clustering, run GSEA show result plots HTML reports. time-series data valid input limma package also valid input SplineOmics package (transcriptomics, proteomics, phosphoproteomics, metabolomics, glycan fractional abundances, etc.).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"what-do-i-need-precisely","dir":"","previous_headings":"π Introduction","what":"What do I need precisely?","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Data: data matrix row feature (e.g., protein, metabolite, etc.) column sample taken specific time. data must NA values, normally distributed features dependence samples. Meta: table metadata columns/samples data matrix (e.g., batch, time point, etc.) Annotation (optional): table identifiers rows/features data matrix (e.g., gene protein name).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"capabilities","dir":"","previous_headings":"π Introduction","what":"Capabilities","title":"Streamlines the process of analysing omics timeseries data with splines","text":"SplineOmics, can: Automatically perform exploratory data analysis: explore_data() function generates HTML report, containing various plots, density, PCA, correlation heatmap plots (example report). Explore various limma splines hyperparameters: Test combinations hyperparameters, different datasets, limma design formulas, degrees freedom, p-value thresholds, etc., using screen_limma_hyperparams() function (example report (along encoding)). Perform limma spline analysis: Use run_limma_splines() function perform limma analysis splines optimal hyperparameters identified (example report). Cluster significant features: Cluster significant features (hits) identified spline analysis cluster_hits() function (example report). Run GSEA clustered hits: Perform gene set enrichment analysis (GSEA) using clustered hits create_gsea_report() function (example report).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-installation","dir":"","previous_headings":"","what":"π§ Installation","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Follow steps install SplineOmics package GitHub repository R environment.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"prerequisites","dir":"","previous_headings":"π§ Installation","what":"Prerequisites","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Ensure R installed system. , download install CRAN. RStudio recommended user-friendly experience R. Download install RStudio posit.co.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"installation-steps","dir":"","previous_headings":"π§ Installation","what":"Installation Steps","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Note Windows Users: Note installation paths potentially writable Windows. Therefore, can necessary set library path use path installations: Alternatively, can run RStudio administrator installation (however generally recommended, security risk). Open RStudio R console. Install BiocManager Bioconductor dependencies (already installed) Install Bioconductor dependencies separately using BiocManager Install remotes package GitHub downloads (already installed) Install SplineOmics package GitHub non-Bioconductor dependencies, using remotes Verify installation SplineOmics package","code":"# Define the custom library path and expand the tilde (~) custom_lib_path <- path.expand(\"~/Rlibs\") # Create the directory if it doesn't exist if (!dir.exists(custom_lib_path)) { dir.create( custom_lib_path, showWarnings = FALSE, recursive = TRUE ) } # Set the library path to include the new directory .libPaths(c(custom_lib_path, .libPaths())) # Check if the new library path is added successfully if (custom_lib_path %in% .libPaths()) { message(\"Library path set to: \", custom_lib_path) } else { stop(\"Failed to set library path.\") } install.packages( \"BiocManager\" # lib = custom_lib_path ) BiocManager::install( c(\"ComplexHeatmap\", \"limma\") # force = TRUE # when encountering issues # lib = custom_lib_path ) install.packages( \"remotes\" # lib = custom_lib_path ) remotes::install_github( \"csbg/SplineOmics\", # GitHub repository ref = \"0.1.0\", # Specify the tag to install dependencies = TRUE, # Install all dependencies upgrade = \"always\" # Always upgrade dependencies # force = TRUE # when encountering issues # lib = custom_lib_path ) # Verify the installation of the SplineOmics package if (\"SplineOmics\" %in% rownames(installed.packages())) { message(\"SplineOmics was installed successfully.\") } else { message(\"SplineOmics installation failed. Please check for errors during installation.\") }"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"troubleshooting","dir":"","previous_headings":"π§ Installation","what":"Troubleshooting","title":"Streamlines the process of analysing omics timeseries data with splines","text":"encounter errors related dependencies package versions installation, try updating R RStudio latest versions repeat installation steps. issues specifically related SplineOmics package, check Issues section GitHub repository similar problems post new issue.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-docker-container","dir":"","previous_headings":"π§ Installation","what":"π³ Docker Container","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Alternatively, can run analysis Docker container. underlying Docker image encapsulates SplineOmics package together necessary environment dependencies. ensures higher levels reproducibility analysis carried consistent environment, independent operating system custom configurations. Please note must Docker Engine installed machine. instructions install , consult official Docker Engine installation guide. information Docker containers can found official Docker page. instructions downloading image SplineOmics package running container, please refer Docker instructions.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"troubleshooting-1","dir":"","previous_headings":"π§ Installation > π³ Docker Container","what":"Troubleshooting","title":"Streamlines the process of analysing omics timeseries data with splines","text":"face βpermission deniedβ issues Linux distributions, check vignette.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"tutorial","dir":"","previous_headings":"βΆ Usage","what":"Tutorial","title":"Streamlines the process of analysing omics timeseries data with splines","text":"tutorial covers real CHO cell time-series proteomics example start end. open R Markdown file tutorial RStudio, run: open R Markdown file RStudio containing template analysis, run:","code":"library(SplineOmics) open_tutorial() library(SplineOmics) open_template()"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"details","dir":"","previous_headings":"βΆ Usage","what":"Details","title":"Streamlines the process of analysing omics timeseries data with splines","text":"detailed description arguments outputs functions package (exported internal functions) can found .","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"design-limma-design-formula","dir":"","previous_headings":"βΆ Usage > Details","what":"Design limma design formula","title":"Streamlines the process of analysing omics timeseries data with splines","text":"quick guide design limma design formula can found explanation three different limma results ","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"rna-seq-data","dir":"","previous_headings":"βΆ Usage > RNA-seq and Glycan Data","what":"RNA-seq data","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Transcriptomics data must preprocessed limma. need provide appropriate object, voom object, rna_seq_data argument SplineOmics object (see documentation). Along , normalized matrix (e.g., $E slot voom object) must passed data argument. allows flexibility preprocessing; can use method prefer long final object matrix compatible limma. One way preprocess RNA-seq data using preprocess_rna_seq_data() function included SplineOmics package (see documentation).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"glycan-fractional-abundance-data","dir":"","previous_headings":"βΆ Usage > RNA-seq and Glycan Data","what":"Glycan fractional abundance data","title":"Streamlines the process of analysing omics timeseries data with splines","text":"glycan fractional abundance data matrix, row represents type glycan columns correspond timepoints, must transformed analysis. preprocessing step essential due compositional nature data. compositional data, increase abundance one component (glycan) necessarily results decrease others, introducing dependency among variables can bias analysis. One way address issue applying Centered Log Ratio (CLR) transformation data clr function compositions package: results clr transformed data can harder understand interpret however. prefer ease interpretation fine results contain artifacts due compositional nature data, log2 transform data instead use input SplineOmics package.","code":"library(compositions) clr_transformed_data <- clr(data_matrix) # use as SplineOmics input log2_transformed_data <- log2(data_matrix) # use as SplineOmics input"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-dependencies","dir":"","previous_headings":"","what":"π¦ Dependencies","title":"Streamlines the process of analysing omics timeseries data with splines","text":"SplineOmics package relies several R packages functionality. list dependencies automatically installed along SplineOmics. already packages installed, ensure date avoid compatibility issues. ComplexHeatmap (>= 2.18.0): creating complex heatmaps advanced features. base64enc (>= 0.1-3): encoding/decoding base64. dendextend (>= 1.17.1): extending dendrogram objects, allowing easier manipulation dendrograms. dplyr (>= 1.1.4): data manipulation. ggplot2 (>= 3.5.1): creating elegant data visualizations using grammar graphics. ggrepel (>= 0.9.5): better label placement ggplot2. (>= 1.0.1): constructing paths projectβs files. limma (>= 3.58.1): linear models microarray RNA-seq analysis. openxlsx (>= 4.2.6.1): reading, writing, editing .xlsx files. patchwork (>= 1.2.0): combining multiple ggplot objects single plot. pheatmap (>= 1.0.12): creating aesthetically pleasing heatmaps. progress (>= 1.2.3): adding progress bars loops apply functions. purrr (>= 1.0.2): functional programming tools. rlang (>= 1.1.4): working core language features R. scales (>= 1.3.0): scale functions visualizations. svglite (>= 2.1.3): creating high-quality vector graphics (SVG). tibble (>= 3.2.1): creating tidy data frames. tidyr (>= 1.3.1): tidying data. zip (>= 2.3.1): compressing combining files zip archives.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"optional-dependencies","dir":"","previous_headings":"π¦ Dependencies","what":"Optional Dependencies","title":"Streamlines the process of analysing omics timeseries data with splines","text":"packages optional needed specific functionality: edgeR (>= 4.0.16): preprocessing RNA-seq data preprocess_rna_seq_data() function. clusterProfiler (>= 4.10.1): run_gsea() function (gene set enrichment analysis). rstudioapi (>= 0.16.0): open_tutorial() open_template() functions.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"r-version","dir":"","previous_headings":"π¦ Dependencies","what":"R Version","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Recommended: R 4.3.3 higher","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-further-reading","dir":"","previous_headings":"","what":"π Further Reading","title":"Streamlines the process of analysing omics timeseries data with splines","text":"interested gaining deeper understanding methodologies used SplineOmics package, recommended publications: Splines: learn splines, can refer review. limma: read limma R package, can refer publication. PCA: learn PCA, download read document. Hierarchical clustering: get information hierarchical clustering, can refer web article.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-getting-help","dir":"","previous_headings":"","what":"β Getting Help","title":"Streamlines the process of analysing omics timeseries data with splines","text":"encounter bug suggestion improving SplineOmics package, encourage open issue GitHub repository. opening new issue, please check see question bug already reported another user. helps avoid duplicate reports ensures can address problems efficiently. detailed questions, discussions, contributions regarding packageβs use development, please refer GitHub Discussions page SplineOmics.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-contributing","dir":"","previous_headings":"","what":"π€ Contributing","title":"Streamlines the process of analysing omics timeseries data with splines","text":"welcome contributions SplineOmics package! Whether βre interested fixing bugs, adding new features, improving documentation, help greatly appreciated. βs can contribute: Report Bug Request Feature: encounter bug idea new feature, please open issue GitHub repository. opening new issue, check see issue already reported feature requested another user. Submit Pull Request: βve developed bug fix new feature βd like share, submit pull request. Improve Documentation: Good documentation crucial project. notice missing incorrect documentation, please feel free contribute. Please adhere Code Conduct interactions project. Thank considering contributing SplineOmics. efforts make open-source community fantastic place learn, inspire, create.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-feedback","dir":"","previous_headings":"","what":"π¬ Feedback","title":"Streamlines the process of analysing omics timeseries data with splines","text":"appreciate feedback! Besides raising issues, can provide feedback following ways: Direct Email: Send feedback directly Thomas Rauter. Anonymous Feedback: Use Google Form provide anonymous feedback answering questions. feedback helps us improve project address issues may encounter.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-license","dir":"","previous_headings":"","what":"π License","title":"Streamlines the process of analysing omics timeseries data with splines","text":"package licensed MIT License: LICENSE Β© 2024 Thomas Rauter. rights reserved.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-citation","dir":"","previous_headings":"","what":"π Citation","title":"Streamlines the process of analysing omics timeseries data with splines","text":"SplineOmics package currently published peer-reviewed scientific journal similar outlet. However, package helped work, consider citing GitHub repository. cite package, can use citation information provided CITATION.cff file. can also generate citation various formats using CITATION.cff file visiting top right repo clicking βCite repositoryβ button. Also, like package, consider giving GitHub repository star. support helps us continued development improvement SplineOmics. Thank using package!","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-contributors","dir":"","previous_headings":"","what":"π Contributors","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Thomas-Rauter - π Wrote package, developed approach together VSchaepertoens guidance nfortelny skafdasschaf. nfortelny - π§ Principal Investigator, provided guidance support overall approach. skafdasschaf - π§ Helped reviewing code, delivered improvement suggestions scientific guidance develop approach. VSchaepertoens - β¨ Developed one internal plotting function, well code exploratory data analysis plots, overall approach together Thomas-Rauter.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-acknowledgements","dir":"","previous_headings":"","what":"π Acknowledgements","title":"Streamlines the process of analysing omics timeseries data with splines","text":"work carried context DigiTherapeutX project, funded Austrian Science Fund (FWF). research conducted supervision Prof.Β Nikolaus Fortelny, leads Computational Systems Biology working group Paris Lodron University Salzburg, Austria. can find information Prof.Β Fortelnyβs research group .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":null,"dir":"Reference","previous_headings":"","what":"InputControl: A class for controlling and validating inputs β InputControl","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"InputControl: class controlling validating inputs InputControl: class controlling validating inputs","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"class provides methods validate inputs function. method performs following checks: * Ensures `annotation` `data` provided. * Confirms `annotation` dataframe. * Verifies `annotation` `data` number rows. checks fail, informative error message returned. function performs following checks: - `clusters` integer vector integers. Otherwise, gives error. Check Plot Info method performs following checks: * Ensures `plot_info` provided NULL. * Confirms `y_axis_label` character vector maximally 30 characters. * Confirms `time_unit` character vector maximally 15 characters. * Validates `treatment_labels` either `NA` character vector element maximally 15 characters long. * Validates `treatment_timepoints` either `NA` numeric vector length `treatment_labels` `treatment_labels` `NA`. checks fail, informative error message returned. function performs following checks: 1. Ensures `feature_name_columns` `annotation` `NULL`. 2. Verifies element `feature_name_columns` character length 1. 3. Checks elements `feature_name_columns` valid column names `annotation` data frame. Check Report function performs following checks: - Whether `report` argument present. - `report` Boolean value (`TRUE` `FALSE`), throws error.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"InputControl: method verifies spline test configurations associated metadata within object's arguments. performs series checks configurations, including column verification, spline type validation, ensuring degrees freedom (dof) within acceptable ranges.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"SplineOmics::Level4Functions -> SplineOmics::Level3Functions -> SplineOmics::Level2Functions -> InputControl","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"args list arguments validated. Initialize InputControl object","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"SplineOmics::Level4Functions$create_error_message() SplineOmics::Level3Functions$check_batch_column() SplineOmics::Level3Functions$check_condition_time_consistency() SplineOmics::Level3Functions$check_voom_structure() SplineOmics::Level2Functions$check_columns() SplineOmics::Level2Functions$check_columns_spline_test_configs() SplineOmics::Level2Functions$check_data() SplineOmics::Level2Functions$check_dataframe() SplineOmics::Level2Functions$check_max_and_min_dof() SplineOmics::Level2Functions$check_meta() SplineOmics::Level2Functions$check_spline_params_generally() SplineOmics::Level2Functions$check_spline_params_mode_dependent() SplineOmics::Level2Functions$check_spline_type_column() SplineOmics::Level2Functions$check_spline_type_params()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"InputControl$new() InputControl$auto_validate() InputControl$check_data_and_meta() InputControl$check_annotation() InputControl$check_datas_and_metas() InputControl$check_datas_descr() InputControl$check_top_tables() InputControl$check_design_formula() InputControl$check_dream_params() InputControl$check_modes() InputControl$check_mode() InputControl$check_designs_and_metas() InputControl$check_spline_params() InputControl$check_spline_test_configs() InputControl$check_limma_top_tables() InputControl$check_adj_pthresholds() InputControl$check_adj_pthresh_limma_category_2_3() InputControl$check_clusters() InputControl$check_plot_info() InputControl$check_plot_options() InputControl$check_raw_data() InputControl$check_report_dir() InputControl$check_genes() InputControl$check_padjust_method() InputControl$check_report_info() InputControl$check_feature_name_columns() InputControl$check_report() InputControl$clone()","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$new(args)"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"args list arguments validated.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"new instance InputControl class. Automatically Validate Arguments method automatically validates arguments sequentially calling various validation methods defined within class. validation method checks specific aspects input arguments raises error validation fails. following validation methods called sequence: - self$check_data_and_meta() - self$check_datas_and_metas() - self$check_datas_descr() - self$check_design_formula() - self$check_mode() - self$check_modes() - self$check_designs_and_metas() - self$check_spline_params() - self$check_spline_test_configs() - self$check_adj_pthresholds() - self$check_clusters() - self$check_time_unit() - self$check_raw_data() - self$check_report_dir() - self$check_padjust_method() - self$check_report_info() - self$check_report() - self$check_feature_name_columns()","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$auto_validate()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL. function used side effects validating input arguments raising errors validation fails. Check Data Meta","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-data-and-meta-","dir":"Reference","previous_headings":"","what":"Method check_data_and_meta()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity data meta objects, ensuring data matrix numeric values meta dataframe containing specified condition column. Additionally, verifies number columns data matrix matches number rows meta dataframe.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_data_and_meta()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"data matrix containing numeric values. meta dataframe containing metadata, including 'Time' column specified condition column. condition single character string specifying column name meta dataframe checked. meta_batch_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. data_meta_index optional parameter specifying index data/meta pair error messages. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check Annotation Consistency","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-annotation-","dir":"Reference","previous_headings":"","what":"Method check_annotation()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"method checks consistency annotation data. ensures annotation dataframe number rows data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_annotation()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL required arguments missing. Otherwise, performs checks potentially raises errors checks fail. Check Multiple Data Meta Pairs","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-datas-and-metas-","dir":"Reference","previous_headings":"","what":"Method check_datas_and_metas()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Iterates multiple data meta pairs validate pair using `check_data_and_meta` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_datas_and_metas()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"datas list matrices containing numeric values. metas list data frames containing metadata. condition character string specifying column name meta dataframe checked. meta_batch_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. meta_batch2_column optional parameter specifying column name meta dataframe used remove second batch effect. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-4","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL check fails, otherwise returns TRUE. Check Data Descriptions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-datas-descr-","dir":"Reference","previous_headings":"","what":"Method check_datas_descr()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates data descriptions character vectors element exceeding 80 characters length.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_datas_descr()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"datas_descr character vector data descriptions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-5","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-top-tables-","dir":"Reference","previous_headings":"","what":"Method check_top_tables()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates top tables list dataframes checks dataframe using `check_dataframe` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_top_tables()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"top_tables list top tables limma analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-6","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"return value, called side effects. Check Design Formula","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-design-formula-","dir":"Reference","previous_headings":"","what":"Method check_design_formula()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates design formula ensuring valid character string, contains allowed characters, includes intercept term 'X', references columns present metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_design_formula()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"formula character string representing design formula. meta data frame containing metadata. meta_index optional index data/meta pair.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-7","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"TRUE design formula valid, otherwise error thrown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-dream-params-","dir":"Reference","previous_headings":"","what":"Method check_dream_params()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity `dream_params` argument provided class. `dream_params` present, ensures contains required optional elements correct format. Specifically, `dream_params` must contain named element `random_effects`, required must string. may also optionally contain elements `dof`, must integer greater 1, `KenwardRoger`, must boolean. Unnamed elements elements three allowed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_dream_params()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-8","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `TRUE` `dream_params` passes checks. Otherwise, stops function returns error message using `stop_call_false`. Validate check modes","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-modes-","dir":"Reference","previous_headings":"","what":"Method check_modes()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function iterates `modes` argument, sets `mode` `self$args`, calls `check_mode()` validate mode. validation, `mode` removed `self$args`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_modes()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-9","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL `modes` missing; otherwise, checks modes. Check mode argument validity","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-mode-","dir":"Reference","previous_headings":"","what":"Method check_mode()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks `mode` argument provided validates either \"isolated\" \"integrated\". `mode` missing invalid, error thrown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_mode()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-10","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL `mode` missing; otherwise, validates mode. Check Multiple Designs Metas","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-designs-and-metas-","dir":"Reference","previous_headings":"","what":"Method check_designs_and_metas()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Iterates multiple design formulas corresponding metadata validate pair using `check_design_formula` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-11","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_designs_and_metas()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"designs vector character strings representing design formulas. metas list data frames containing metadata. meta_indices vector optional indices data/meta pairs.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-11","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL check fails, otherwise returns TRUE. Check Spline Parameters","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-spline-params-","dir":"Reference","previous_headings":"","what":"Method check_spline_params()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates spline parameters generally depending specified mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-12","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_spline_params()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"spline_params list spline parameters. mode character string specifying mode ('integrated' 'isolated'). meta dataframe containing metadata. condition character string specifying condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-12","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `NULL` required arguments mising, otherwise, called side effects. Check Spline Test Configurations","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-13","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_spline_test_configs()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"spline_test_configs configuration object spline tests. metas list metadata corresponding data matrices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-13","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `NULL` required arguments mising, otherwise, called side effects. Check Limma Top Tables Structure function checks provided limma top tables data structure correctly formatted. ensures data structure contains exactly three named elements ('time_effect', 'avrg_diff_conditions', 'interaction_condition_time') element contains dataframes correct columns data types.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-14","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_limma_top_tables()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"self object containing data structure check.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-14","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function return value. stops execution data structure match expected format. Check Adjusted p-Thresholds","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-adj-pthresholds-","dir":"Reference","previous_headings":"","what":"Method check_adj_pthresholds()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity adjusted p-thresholds vector, ensuring elements numeric, greater 0, less 1. conditions met, function stops execution returns error message indicating offending elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-15","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_adj_pthresholds()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"adj_pthresholds numeric vector adjusted p-thresholds.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-15","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check adjusted p-value thresholds limma category 2 3","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-adj-pthresh-limma-category-","dir":"Reference","previous_headings":"","what":"Method check_adj_pthresh_limma_category_2_3()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks adjusted p-value thresholds average difference conditions interaction condition time non-null, floats, range [0, 1].","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-16","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_adj_pthresh_limma_category_2_3()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-16","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"`NULL` either argument `NULL` invalid. Otherwise, return value (assumed valid inputs). Check Clusters","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-clusters-","dir":"Reference","previous_headings":"","what":"Method check_clusters()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function verifies cluster configurations within object's arguments. checks clusters argument present performs validation content. clusters specified, defaults automatic cluster estimation.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-17","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_clusters()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-plot-info-","dir":"Reference","previous_headings":"","what":"Method check_plot_info()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"method checks validity `plot_info` list. ensures `y_axis_label` `time_unit` meet length constraints, `treatment_labels` either `NA` character vector elements meeting length constraint, `treatment_timepoints` either `NA` numeric vector length `treatment_labels`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-18","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_plot_info()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-17","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL `plot_info` provided invalid. Otherwise, performs checks potentially raises errors checks fail. Check plot options","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-plot-options-","dir":"Reference","previous_headings":"","what":"Method check_plot_options()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"method checks `plot_options` list contains required elements `meta_replicate_column` `cluster_heatmap_columns`. validates `cluster_heatmap_columns` either TRUE FALSE, `meta_replicate_column` valid column name `meta` dataframe. checks fail, script stops error message. Check Raw Data Validity","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-19","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_plot_options()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-raw-data-","dir":"Reference","previous_headings":"","what":"Method check_raw_data()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function verifies `raw_data` argument ensure numeric matrix dimensions `data`. NA values permitted within `raw_data`. `raw_data` numeric matrix match dimensions `data`, function stops execution error.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-20","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_raw_data()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-18","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns NULL either `data` `raw_data` provided checks pass. Stops execution raises error `raw_data` meet criteria. Check Create Report Directory","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-report-dir-","dir":"Reference","previous_headings":"","what":"Method check_report_dir()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks specified report directory exists valid directory. directory exist, attempts create . warnings errors directory creation, function stops execution returns error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-21","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_report_dir()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-11","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"report_dir character string specifying path report directory.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-19","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE directory exists successfully created. Stops execution returns error message directory created valid. Check Genes Validity","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-genes-","dir":"Reference","previous_headings":"","what":"Method check_genes()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity `data` `genes` arguments within `self$args` list. ensures `genes` character vector, neither `data` `genes` `NULL`, length `genes` matches number rows `data`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-22","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_genes()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-20","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `TRUE` checks pass. Returns `NULL` required arguments `NULL`. Throws error `genes` character vector length `genes` match number rows `data`. Check p-Adjustment Method","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-padjust-method-","dir":"Reference","previous_headings":"","what":"Method check_padjust_method()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks provided p-adjustment method valid. valid methods : \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"\", \"fdr\", \"none\". method one , function stops execution returns error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-23","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_padjust_method()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-12","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"padjust_method character string specifying p-adjustment method. Valid options \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"\", \"fdr\", \"none\".","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-21","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE p-adjustment method valid. Stops execution returns error message method invalid. Check Report Information","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-report-info-","dir":"Reference","previous_headings":"","what":"Method check_report_info()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates report information ensure contains mandatory fields adheres required formats.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-24","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_report_info()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-13","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"report_info named list containing report information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-22","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"TRUE report information valid; otherwise, error thrown. Check Feature Name Columns","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-feature-name-columns-","dir":"Reference","previous_headings":"","what":"Method check_feature_name_columns()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks whether elements `feature_name_columns` characters length 1 whether valid column names `annotation` data frame.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-25","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_feature_name_columns()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-23","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `NULL` required arguments missing. Throws error element `feature_name_columns` character length 1 element column name `annotation`. Returns `TRUE` checks pass.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-report-","dir":"Reference","previous_headings":"","what":"Method check_report()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function verifies `report` argument within object's arguments. checks `report` argument present validates Boolean value.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-26","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_report()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-27","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-14","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Level2Functions: A class providing level 2 functionalities β Level2Functions","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Level2Functions: class providing level 2 functionalities Level2Functions: class providing level 2 functionalities","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"class provides various level 2 functionalities, including methods check dataframes spline parameters.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"SplineOmics::Level4Functions -> SplineOmics::Level3Functions -> Level2Functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"SplineOmics::Level4Functions$create_error_message() SplineOmics::Level3Functions$check_batch_column() SplineOmics::Level3Functions$check_condition_time_consistency() SplineOmics::Level3Functions$check_voom_structure()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Level2Functions$check_data() Level2Functions$check_meta() Level2Functions$check_dataframe() Level2Functions$check_spline_params_generally() Level2Functions$check_spline_params_mode_dependent() Level2Functions$check_columns_spline_test_configs() Level2Functions$check_spline_type_column() Level2Functions$check_spline_type_params() Level2Functions$check_max_and_min_dof() Level2Functions$check_columns() Level2Functions$clone()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-data-","dir":"Reference","previous_headings":"","what":"Method check_data()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"function checks validity data matrix, ensuring matrix, contains numeric values, missing values, elements non-negative. Additionally, verifies rows columns entirely zeros.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_data(data, data_meta_index = NULL)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"data dataframe containing numeric values. data_meta_index optional parameter specifying index data error messages. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check Metadata","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-meta-","dir":"Reference","previous_headings":"","what":"Method check_meta()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"function checks validity metadata dataframe, ensuring contains 'Time' column, contain missing values, specified condition column valid appropriate type. Additionally, checks optional batch effect column prints messages regarding use.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_meta( meta, condition, meta_batch_column = NULL, meta_batch2_column = NULL, data_meta_index = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"meta dataframe containing metadata, including 'Time' column. condition single character string specifying column name meta dataframe checked. meta_batch_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. meta_batch2_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. data_meta_index optional parameter specifying index data/meta pair error messages. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check Dataframe","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-dataframe-","dir":"Reference","previous_headings":"","what":"Method check_dataframe()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates dataframe contains required columns correct data types.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_dataframe(df)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"df dataframe check.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"TRUE dataframe valid, otherwise error thrown. Check Spline Parameters Generally","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-params-generally-","dir":"Reference","previous_headings":"","what":"Method check_spline_params_generally()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates general structure contents spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_params_generally(spline_params)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_params list spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects. Check Spline Parameters Mode Dependent","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-params-mode-dependent-","dir":"Reference","previous_headings":"","what":"Method check_spline_params_mode_dependent()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates spline parameters depending specified mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_params_mode_dependent( spline_params, mode, meta, condition )"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_params list spline parameters. mode character string specifying mode ('integrated' 'isolated'). meta dataframe containing metadata. condition character string specifying condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-4","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects. Check Columns Spline Test Configurations","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-columns-spline-test-configs-","dir":"Reference","previous_headings":"","what":"Method check_columns_spline_test_configs()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates spline test configurations contain required columns correct order.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_columns_spline_test_configs(spline_test_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-5","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-type-column-","dir":"Reference","previous_headings":"","what":"Method check_spline_type_column()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates 'spline_type' column spline test configurations contains 'n' 'b'.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_type_column(spline_test_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-6","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-type-params-","dir":"Reference","previous_headings":"","what":"Method check_spline_type_params()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates parameters row spline test configurations based spline type.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_type_params(spline_test_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-7","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"TRUE checks pass, otherwise error thrown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-max-and-min-dof-","dir":"Reference","previous_headings":"","what":"Method check_max_and_min_dof()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates degrees freedom (DoF) row spline test configurations based metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_max_and_min_dof(spline_test_configs, metas)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations. metas list metadata corresponding data matrices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-8","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_columns(df, expected_cols)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"df dataframe check. expected_cols character vector expected column names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-9","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"function return value. stops execution dataframe columns classes match expected structure.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Level3Functions: A class for level 3 utility functions β Level3Functions","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Level3Functions: class level 3 utility functions Level3Functions: class level 3 utility functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"class provides methods creating error messages checking batch columns. function verifies `voom` object contains following components: - `E`: matrix log2-counts per million (logCPM) values. - `weights`: matrix observation-specific weights matches dimensions `E`. - `design`: matrix representing design matrix used linear modeling, number rows columns `E`. function also checks optional components : - `genes`: data frame gene annotations. - `targets`: data frame target information. - `sample.weights`: numeric vector sample-specific weights. checks fail, function stops reports issues. structure valid, message confirming validity printed.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"SplineOmics::Level4Functions -> Level3Functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"SplineOmics::Level4Functions$create_error_message()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Level3Functions$check_voom_structure() Level3Functions$check_batch_column() Level3Functions$check_condition_time_consistency() Level3Functions$clone()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-check-voom-structure-","dir":"Reference","previous_headings":"","what":"Method check_voom_structure()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"function checks structure `voom` object ensure contains expected components components correct types dimensions. function check actual data within matrices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$check_voom_structure(voom_obj)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"voom_obj list representing `voom` object, typically created `voom` function `limma` package.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Boolean TRUE FALSE. However, function mostly called side effects, stop script structure valid. Check Batch Column","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-check-batch-column-","dir":"Reference","previous_headings":"","what":"Method check_batch_column()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"method checks batch column metadata provides appropriate messages.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$check_batch_column(meta, meta_batch_column, data_meta_index)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"meta dataframe containing metadata. meta_batch_column character string specifying batch column metadata. data_meta_index optional parameter specifying index data/meta pair. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"NULL. method used side effects throwing errors printing messages. Check Condition Time Consistency","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-check-condition-time-consistency-","dir":"Reference","previous_headings":"","what":"Method check_condition_time_consistency()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"function checks whether values `condition` column unique values block identical `Time` values `meta` dataframe. Additionally, ensures every new block given time new value `condition` column.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$check_condition_time_consistency(meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"meta dataframe containing metadata, including `Time` column. condition character string specifying column name `meta` used define groups analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Logical TRUE condition values consistent time series pattern.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Level4Functions: A class for level 3 utility functions β Level4Functions","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"Level4Functions: class level 3 utility functions Level4Functions: class level 3 utility functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"class provides methods creating error messages checking batch columns.","code":""},{"path":[]},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"Level4Functions$create_error_message() Level4Functions$clone()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"method-create-error-message-","dir":"Reference","previous_headings":"","what":"Method create_error_message()","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"method creates formatted error message includes index data/meta pair provided. index provided, returns message .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"","code":"Level4Functions$create_error_message(message, data_meta_index = NULL)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"message character string specifying error message. data_meta_index optional parameter specifying index data/meta pair error message. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"Returns formatted error message string. index provided, message includes index; otherwise, returns message .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"","code":"Level4Functions$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":null,"dir":"Reference","previous_headings":"","what":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"class provides methods identify upper-left lower-right cells numeric block within dataframe.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"data dataframe containing input data. upper_left_cell list containing row column indices upper-left cell. Initialize NumericBlockFinder object","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"NumericBlockFinder$new() NumericBlockFinder$find_upper_left_cell() NumericBlockFinder$find_lower_right_cell() NumericBlockFinder$clone()","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$new(data)"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"data dataframe containing input data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"new instance NumericBlockFinder class. Find upper-left cell first 6x6 block numeric values method identifies upper-left cell first 6x6 block numeric values dataframe.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$find_upper_left_cell()"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"list containing row column indices upper-left cell. Find lower-right cell block contiguous non-NA values method identifies lower-right cell block contiguous non-NA values starting given upper-left cell dataframe.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$find_lower_right_cell()"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"list containing row column indices lower-right cell.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Package Name: SplineOmics β SplineOmics-package","title":"Package Name: SplineOmics β SplineOmics-package","text":"R package SplineOmics finds significant features (hits) time-series -omics data using splines limma hypothesis testing. clusters hits based spline shape showing results summary HTML reports. detailed documentation, vignettes, examples, please visit [SplineOmics GitHub page](https://github.com/csbg/SplineOmics.git).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"key-functions-and-classes","dir":"Reference","previous_headings":"","what":"Key Functions and Classes","title":"Package Name: SplineOmics β SplineOmics-package","text":"- extract_data: Extracts data matrix Excel file. - create_splineomics: Creates SplineOmics object, contains arguments used several package functions. - explore_data: Performs exploratory data analysis data, outputs HTML report containg various plots, density plots correlation heatmaps. - screen_limma_hyperparams: Allows specify lists different hyperparameters test, degree freedom 2, 3, 4, adj.p-val thresholds, 0.1 0.05, tests specified different values limma spline hyperparameters semi-combinatorial way. - update_splineomics: Allows change values SplineOmics object, example observing outliers removed data (update data parameter). - run_limma_splines: Central function script, called screen_limma_hyperparams function can called get limma spline analysis results (p-values features (e.g. proteins)) hyperparameters, selected finally. - create_limma_report: Creates HTML report showing run_limma_splines results - cluster_hits: Clusters splines hits (significant features) based shape shows results plots HTML report. - download_enrichr_databases: Allows download Enrichr databases runnin clusterProfiler run_gsea function . - run_gsea: Runs clusterProfiler clustered hits using Enrichr databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package Options","title":"Package Name: SplineOmics β SplineOmics-package","text":"None","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"dependencies","dir":"Reference","previous_headings":"","what":"Dependencies","title":"Package Name: SplineOmics β SplineOmics-package","text":"- **ComplexHeatmap**: creating complex heatmaps advanced features. - **base64enc**: encoding/decoding base64. - **dendextend**: extending `dendrogram` objects R, allowing easier manipulation dendrograms. - **dplyr**: data manipulation. - **ggplot2**: creating elegant data visualizations using grammar graphics. - **ggrepel**: better label placement ggplot2. - ****: constructing paths projectβs files. - **limma**: linear models microarray data. - **openxlsx**: reading, writing, editing xlsx files. - **patchwork**: combining multiple ggplot objects single plot. - **pheatmap**: creating pretty heatmaps. - **progress**: adding progress bars loops apply functions. - **purrr**: functional programming tools. - **rlang**: tools work core language features R Rβs base types. - **scales**: scale functions visualization. - **tibble**: creating tidy data frames easy work . - **tidyr**: tidying data. - **zip**: combining files zip file. Optional dependencies dependencies necessary functions: - **edgeR**: preprocessing RNA-seq data run_limma_splines() fun. - **clusterProfiler**: run_gsea() function (gene set enrichment). - **rstudioapi**: open_tutorial() open_template() functions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"authors","dir":"Reference","previous_headings":"","what":"Authors","title":"Package Name: SplineOmics β SplineOmics-package","text":"- [Thomas-Rauter](https://github.com/Thomas-Rauter) - Wrote package developed approach VSchaepertoens guidance nfortelny skafdasschaf. - [nfortelny](https://github.com/nfortelny) - Principal Investigator, provided guidance support. - [skafdasschaf](https://github.com/skafdasschaf) - Helped review code provided improvement suggestions. - [VSchaepertoens](https://github.com/VSchaepertoens) - Developed internal plotting function contributed exploratory data analysis overall approach.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"maintainer","dir":"Reference","previous_headings":"","what":"Maintainer","title":"Package Name: SplineOmics β SplineOmics-package","text":"- Name: Thomas Rauter - Email: thomas.rauter@plus.ac.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"license","dir":"Reference","previous_headings":"","what":"License","title":"Package Name: SplineOmics β SplineOmics-package","text":"- License: MIT","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"useful-urls","dir":"Reference","previous_headings":"","what":"Useful URLs","title":"Package Name: SplineOmics β SplineOmics-package","text":"- [GitHub repo package](https://github.com/csbg/SplineOmics.git)","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"additional-information","dir":"Reference","previous_headings":"","what":"Additional Information","title":"Package Name: SplineOmics β SplineOmics-package","text":"None","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Package Name: SplineOmics β SplineOmics-package","text":"Maintainer: Thomas Rauter thomas.rauter@plus.ac.(ORCID)","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"internal function adds dashed vertical lines specified treatment timepoints plot, along text labels display corresponding x-axis values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"","code":"add_dashed_lines( p, treatment_timepoints, treatment_labels, y_pos = 1, horizontal_labels = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"p ggplot object. plot dashed lines labels added. treatment_timepoints numeric vector timepoints dashed lines drawn. treatment_labels character vector labels corresponding treatment timepoint. labels used coloring lines, x-axis coordinates displayed labels. y_pos numeric value specifying y-axis position text labels placed. horizontal_labels Boolean flag indicating whether vertical label (default) horizontal label.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"ggplot object added dashed lines labels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Feature Names to Data β add_feature_names","title":"Add Feature Names to Data β add_feature_names","text":"function assigns feature names rows dataframe based specified column another dataframe. column specified, assigns sequential numbers feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Feature Names to Data β add_feature_names","text":"","code":"add_feature_names(data, clean_data, feature_name_columns)"},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Feature Names to Data β add_feature_names","text":"data dataframe containing original data feature names. clean_data dataframe feature names added. feature_name_columns string specifying name feature columns `data`. `NA`, sequential numbers used feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Feature Names to Data β add_feature_names","text":"`clean_data` dataframe updated row names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Feature Names to Data β add_feature_names","text":"function performs following operations: - Extracts feature names specified column `data`, ignoring `NA` values. - Ensures feature names unique match number rows `clean_data`. - Assigns feature names rows `clean_data`. - `feature_name_column` `NA`, assigns sequential numbers (1, 2, 3, etc.) feature names issues message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Plot to HTML Content β add_plot_to_html","title":"Add Plot to HTML Content β add_plot_to_html","text":"function converts plot base64 image adds HTML content.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Plot to HTML Content β add_plot_to_html","text":"","code":"add_plot_to_html(html_content, plot_element, plots_size, section_index)"},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Plot to HTML Content β add_plot_to_html","text":"html_content current HTML content character string. plot_element plot element converted base64. plots_size integer specifying height plot. section_index integer specifying section index.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Plot to HTML Content β add_plot_to_html","text":"updated HTML content character string.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":null,"dir":"Reference","previous_headings":"","what":"Prompt the user with a yes/no question β ask_user","title":"Prompt the user with a yes/no question β ask_user","text":"function prompts user yes/question. user answers \"yes\" (case insensitive), code proceeds. user answers \"\" anything else, code stops.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prompt the user with a yes/no question β ask_user","text":"","code":"ask_user(question)"},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prompt the user with a yes/no question β ask_user","text":"question string question ask user.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prompt the user with a yes/no question β ask_user","text":"None.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Between Level Analysis β between_level","title":"Between Level Analysis β between_level","text":"Performs -level analysis using LIMMA compare specified levels within condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Between Level Analysis β between_level","text":"","code":"between_level( data, rna_seq_data, meta, design, dream_params, spline_params, condition, compared_levels, padjust_method, feature_names )"},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Between Level Analysis β between_level","text":"data matrix data values. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta dataframe containing metadata, including 'Time' column. design design formula matrix LIMMA analysis. dream_params named list NULL. NULL, must least contain named element 'random_effects', must contain string formula random effects mixed models dream. Additionally, can contain named elements dof, must int bigger 1, degree freedom dream topTable, named element KenwardRoger, must bool, specifying whether use method . spline_params list spline parameters analysis. condition character string specifying condition. compared_levels vector levels within condition compare. padjust_method character string specifying p-adjustment method. feature_names non-empty character vector feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Between Level Analysis β between_level","text":"list containing top tables factor factor-time contrast.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Bind Data with Annotation β bind_data_with_annotation","title":"Bind Data with Annotation β bind_data_with_annotation","text":"function converts matrix dataframe, adds row names first column, binds annotation data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bind Data with Annotation β bind_data_with_annotation","text":"","code":"bind_data_with_annotation(data, annotation = NULL)"},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bind Data with Annotation β bind_data_with_annotation","text":"data matrix containing numeric data. annotation dataframe containing annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bind Data with Annotation β bind_data_with_annotation","text":"dataframe `data` `annotation` combined, row names `data` first column named `feature_names`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Cluster Hits Report β build_cluster_hits_report","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"Generates HTML report clustered hits, including plots spline parameter details, table contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"","code":"build_cluster_hits_report( header_section, plots, limma_result_2_and_3_plots, plots_sizes, level_headers_info, spline_params, adj_pthresholds, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction_condition_time, mode, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"header_section character string containing HTML header section. plots list ggplot2 plot objects. limma_result_2_and_3_plots List containing list lists plots pairwise comparisons condition terms average spline diff interaction condition time, another list lists respective names plot stored. plots_sizes list integers specifying size plot. level_headers_info list header information level. spline_params list spline parameters. adj_pthresholds Float vector values level adj.p.tresh adj_pthresh_avrg_diff_conditions Float adj_pthresh_interaction_condition_time Float mode character string specifying mode ('isolated' 'integrated'). report_info named list containg report info fields. used email hotkey functionality. output_file_path character string specifying path save HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build GSEA Report β build_create_gsea_report","title":"Build GSEA Report β build_create_gsea_report","text":"Generates HTML report Gene Set Enrichment Analysis (GSEA) based provided plot data, header information, content. report includes sections level clustered hits, along table contents various plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build GSEA Report β build_create_gsea_report","text":"","code":"build_create_gsea_report( header_section, plots, plots_sizes, level_headers_info, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build GSEA Report β build_create_gsea_report","text":"header_section string containing HTML content header section report. plots list plots included report. plots_sizes list sizes plots. level_headers_info list containing header information level clustered hits. report_info named list containg report info fields. used email hotkey functionality. output_file_path string specifying file path report saved.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build GSEA Report β build_create_gsea_report","text":"None. function generates writes HTML report specified output file path.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Build GSEA Report β build_create_gsea_report","text":"function first initializes HTML content provided header section placeholder table contents (TOC). iterates plots, generating sections level clustered hits processing individual plots. TOC inserted HTML content, finalized written specified output file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Cluster Hits Report β build_create_limma_report","title":"Build Cluster Hits Report β build_create_limma_report","text":"Generates HTML report clustered hits, including plots spline parameter details, table contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Cluster Hits Report β build_create_limma_report","text":"","code":"build_create_limma_report( header_section, plots, plots_sizes, level_headers_info, report_info, output_file_path = here::here() )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Cluster Hits Report β build_create_limma_report","text":"header_section character string containing HTML header section. plots list ggplot2 plot objects. plots_sizes list integers specifying size plot. level_headers_info list header information level. report_info named list containg report info fields. used email hotkey functionality. output_file_path character string specifying path save HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Cluster Hits Report β build_create_limma_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Explore Data Report β build_explore_data_report","title":"Build Explore Data Report β build_explore_data_report","text":"function generates HTML report containing header section, table contents, series plots. plot included report specified sizes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Explore Data Report β build_explore_data_report","text":"","code":"build_explore_data_report( header_section, plots, plots_sizes, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Explore Data Report β build_explore_data_report","text":"header_section string containing HTML content header section report. plots list ggplot objects representing plots included report. plots_sizes list sizes corresponding plot, defining dimensions used rendering plots. report_info named list containg report info fields. used email hotkey functionality. output_file_path string specifying file path HTML report saved.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Explore Data Report β build_explore_data_report","text":"None. function writes HTML content specified file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"Constructs HTML report hyperparameter screening embedding plots respective sizes provided header section.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"","code":"build_hyperparams_screen_report( header_section, plots, plots_sizes, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"header_section character string containing HTML header section. plots list ggplot2 plot objects. plots_sizes list integers specifying number rows plot. report_info named list containg report info fields. used email hotkey functionality. output_file_path character string specifying path save HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate average CV across unique time points β calc_cv","title":"Calculate average CV across unique time points β calc_cv","text":"function calculates coefficient variation (CV) unique time point based provided time values response values. returns average CV across time points. CV calculated one valid (non-NA) values given time point mean values non-zero.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate average CV across unique time points β calc_cv","text":"","code":"calc_cv(time_values, response_values)"},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate average CV across unique time points β calc_cv","text":"time_values numeric vector containing time points. Time points may repeat across replicates. response_values numeric vector response values corresponding time points.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate average CV across unique time points β calc_cv","text":"average coefficient variation (CV) across time points. Returns NA CVs NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"function checks elements within list top tables contain element names match specified -level pattern.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"","code":"check_between_level_pattern(top_tables)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"top_tables list element list containing named elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"list two elements: between_levels logical value indicating whether element names match -level pattern. index_with_pattern index first element `top_tables` names match -level pattern, NA match found.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"function iterates element `top_tables`. element list, checks names within inner list match pattern `\".+_vs_.+\"`. match found, function sets `between_levels` TRUE records index matching element. search stops first match.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"function checks given dataframe `clustered_genes` contains required columns `gene` `cluster`. `gene` column must contain character strings length 1, `cluster` column must contain integers. condition met, function stops script produces informative error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"","code":"check_clustered_hits(levels_clustered_hits)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"levels_clustered_hits list dataframes checked required format.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"function return value. stops error message conditions met.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Valid Databases Dataframe β check_databases","title":"Check Valid Databases Dataframe β check_databases","text":"function checks dataframe exactly three columns named DB, Geneset, Gene, columns must type character.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Valid Databases Dataframe β check_databases","text":"","code":"check_databases(databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Valid Databases Dataframe β check_databases","text":"databases dataframe check.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Valid Databases Dataframe β check_databases","text":"None. function stops execution provides error message dataframe valid.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Valid Gene IDs β check_genes","title":"Check Valid Gene IDs β check_genes","text":"function checks whether character vector `genes` contains valid gene IDs. gene ID must consist solely alphabetic letters numbers.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Valid Gene IDs β check_genes","text":"","code":"check_genes(genes, max_index_overall = NA)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Valid Gene IDs β check_genes","text":"genes character vector containing gene IDs. max_index_overall integer, specifying highest index features across levels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Valid Gene IDs β check_genes","text":"None. function stops execution provides error message vector meet criteria, including first offending element index.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for NULL Elements in Arguments β check_null_elements","title":"Check for NULL Elements in Arguments β check_null_elements","text":"function checks elements provided list arguments `NULL`. `NULL` elements found, stops execution returns informative error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for NULL Elements in Arguments β check_null_elements","text":"","code":"check_null_elements(args)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for NULL Elements in Arguments β check_null_elements","text":"args list arguments check `NULL` elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for NULL Elements in Arguments β check_null_elements","text":"function return value. stops execution `NULL` elements found input list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Params List for Required Conditions β check_params","title":"Check Params List for Required Conditions β check_params","text":"function checks given list `params` contains allowed named elements. elements present, , must named exactly specified must contain correct data types: float, character, int, int, float. condition met, function stops script produces informative error message. `params` can also `NA`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Params List for Required Conditions β check_params","text":"","code":"check_params(params)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Params List for Required Conditions β check_params","text":"params list checked required conditions, `NA`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Params List for Required Conditions β check_params","text":"function return value. stops error message conditions met.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"function checks given object contains required named elements specified function type. element missing, stops script provides informative error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"","code":"check_splineomics_elements(splineomics, func_type)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"splineomics object checked. func_type string specifying function type. can one \"cluster_hits\", \"create_limma_report\", \"run_limma_splines\", \"explore_data\"","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"None. Stops execution required element missing.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean the Gene Symbols β clean_gene_symbols","title":"Clean the Gene Symbols β clean_gene_symbols","text":"function preprocesses vector gene names cleaning formatting . removes non-alphanumeric characters first block alphanumeric characters converts remaining characters uppercase.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean the Gene Symbols β clean_gene_symbols","text":"","code":"clean_gene_symbols(genes)"},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean the Gene Symbols β clean_gene_symbols","text":"genes character vector containing gene names cleaned.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean the Gene Symbols β clean_gene_symbols","text":"character vector cleaned gene symbols (names) length input. cleaned names uppercase, invalid empty gene names replaced NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster hits (significant features) based on the spline shape β cluster_hits","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"Performs clustering hits top tables generated differential expression analysis. function filters hits based adjusted p-value thresholds, extracts spline coefficients significant features, normalizes coefficients, applies hierarchical clustering. results, including clustering assignments normalized spline curves, saved specified directory compiled HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"","code":"cluster_hits( splineomics, clusters, adj_pthresholds = c(0.05), adj_pthresh_avrg_diff_conditions = 0, adj_pthresh_interaction_condition_time = 0, genes = NULL, plot_info = list(y_axis_label = \"Value\", time_unit = \"min\", treatment_labels = NA, treatment_timepoints = NA), plot_options = list(cluster_heatmap_columns = FALSE, meta_replicate_column = NULL), raw_data = NULL, report_dir = here::here(), report = TRUE )"},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"splineomics S3 object class `SplineOmics` contains necessary data parameters analysis, including: data: original expression dataset used differential expression analysis. meta: dataframe containing metadata corresponding data, must include 'Time' column columns specified conditions. design: character length 1 representing limma design formula. condition: Character length 1 specifying column name meta used define groups analysis. spline_params: list spline parameters analysis. meta_batch_column: character string specifying column name metadata used batch effect removal. meta_batch2_column: character string specifying second column name metadata used batch effect removal. limma_splines_result: list data frames, representing top table differential expression analysis, containing least 'adj.P.Val' expression data columns. clusters Character integer vector specifying number clusters adj_pthresholds Numeric vector p-value thresholds filtering hits top table. adj_pthresh_avrg_diff_conditions p-value threshold results average difference condition limma result. Per default 0 ( turned ). adj_pthresh_interaction_condition_time p-value threshold results interaction condition time limma result. Per default 0 (turned ). genes character vector containing gene names features analyzed. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). plot_options List specific fields (cluster_heatmap_columns = Bool) allow customization plotting behavior. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed. report_dir Character string specifying directory path HTML report output files saved. report Boolean TRUE FALSE value specifing report generated.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"list element corresponds group factor contains clustering results, including `clustered_hits` data frame, hierarchical clustering object `hc`, `curve_values` data frame normalized spline curves, `top_table` cluster assignments.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_create_gsea_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","title":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","text":"Validates inputs generating GSEA report, including clustered hits, genes, databases, parameters, plot titles, background genes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_create_gsea_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","text":"","code":"control_inputs_create_gsea_report( levels_clustered_hits, databases, params, plot_titles, background )"},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_create_gsea_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","text":"levels_clustered_hits list containing clustered hits various levels. databases list databases used GSEA analysis. params list parameters GSEA analysis. plot_titles character vector titles plots, length matching `levels_clustered_hits`. background character vector background genes NULL.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Inputs for Extracting Data β control_inputs_extract_data","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"function checks validity input data feature name column. ensures input data dataframe, feature name column specified correctly, contains valid data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"","code":"control_inputs_extract_data(data, feature_name_columns)"},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"data dataframe containing input data. feature_name_columns character vector specifying names feature name columns. columns must present dataframe data. `NA`, column checked.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"function performs following checks: - Ensures input data dataframe. - Checks feature name column single string exists data. - Ensures specified feature name column contain `NA` values. - Checks input dataframe empty. checks fail, function stops error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"function creates ZIP file containing directories level gene lists. directory contains text files cluster. ZIP file encoded base64 easy download.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"","code":"create_enrichr_zip(enrichr_format)"},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"enrichr_format list formatted gene lists background gene list, typically output `prepare_gene_lists_for_enrichr`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"base64-encoded string representing ZIP file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"function creates temporary directory store files. level `enrichr_format$gene_lists`, creates directory named level. Within level directory, creates text file cluster, containing genes cluster. directories files added ZIP file, encoded base64.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"function conducts Gene Set Enrichment Analysis (GSEA) using either clusterProfiler package. Afterwards, plots results. allows customization enrichment parameters, selection databases, optionally specifying custom plot title background gene list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"","code":"create_gsea_report_level( clustered_genes, databases, params = NA, plot_title = \"\", universe = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"clustered_genes list dataframes two columns: first column contains standard gene symbol, second column contains integer specifying cluster. databases dataframe containing data downloaded Enrichr databases params list specifying clusterProfiler parameters enrichment analysis. plot_title optional string specifying title plot. provided, default title based analysis used. universe optional list standard gene symbols used background enrichment analysis instead background chosen `enricher`. default empty list, implies use default background set enrichment tool.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"object containing results Gene Set Enrichment Analysis, including plots generated analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"Generates HTML report based results limma analysis splines. report includes various plots sections summarizing analysis results time effects, average differences conditions, interaction effects condition time.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"","code":"create_limma_report(splineomics, adj_pthresh = 0.05, report_dir = here::here())"},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"splineomics S3 object class `SplineOmics` contains necessary data parameters analysis, including: limma_splines_result: list containing top tables differential expression analysis three different limma results. meta: data frame sample metadata. Must contain column \"Time\". condition: character string specifying column name metadata (meta) defines groups analysis. column contains levels \"exponential\" \"stationary\" phases, \"drug\" \"no_drug\" treatments. annotation: data frame containing feature information, gene protein names, associated expression data. report_info: list containing metadata analysis reporting purposes. adj_pthresh numeric value specifying adjusted p-value threshold significance. Default 0.05. Must > 0 < 1. report_dir string specifying directory report saved. Default current working directory.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"list plots included generated HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a p-value histogram from a limma top_table β create_p_value_histogram","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"function generates histogram unadjusted p-values limma top_table.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"","code":"create_p_value_histogram(top_table, title = \"P-Value Histogram\")"},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"top_table data frame containing limma top_table column named `P.Value` unadjusted p-values. title character string title histogram.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"ggplot2 object representing histogram unadjusted p-values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":null,"dir":"Reference","previous_headings":"","what":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"Creates progress bar tracking progress iterable task.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"","code":"create_progress_bar(iterable, message = \"Processing\")"},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"iterable iterable object (e.g., list vector) whose length determines total number steps. message message display progress bar (default \"Processing\").","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"progress bar object 'progress' package.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Spline Parameters β create_spline_params","title":"Create Spline Parameters β create_spline_params","text":"Generates spline parameters based configuration, metadata, condition, mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Spline Parameters β create_spline_params","text":"","code":"create_spline_params(spline_test_configs, index, meta, condition, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Spline Parameters β create_spline_params","text":"spline_test_configs configuration object spline tests. index Index spline configuration process. meta dataframe containing metadata. condition character string specifying condition. mode character string specifying mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Spline Parameters β create_spline_params","text":"list processed spline parameters.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"Creates SplineOmics object containing variables commonly used across multiple functions package.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"","code":"create_splineomics( data, meta, condition, rna_seq_data = NULL, annotation = NULL, report_info = NULL, meta_batch_column = NULL, meta_batch2_column = NULL, feature_name_columns = NULL, design = NULL, dream_params = NULL, mode = NULL, spline_params = NULL, padjust_method = \"BH\" )"},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"data actual omics data. case rna_seq_data argument used, still provide argument. case, input data matrix (example $E part voom object). Assign feature names row headers (otherwise, just numbers feature names). meta Metadata associated omics data. condition condition variable. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. argument subjected input control. Rather, regard relies input control `limma::lmfit` function. annotation dataframe feature descriptions data (optional). report_info list containing report information omics data type, data description, data collection date, analyst name, contact info, project name (optional). meta_batch_column Column meta batch information (optional). meta_batch2_column Column secondary meta batch information (optional). feature_name_columns Character vector containing column names annotation info describe features. argument used specify HTML report exactly feature names displayed individual spline plot created. Use vector used create row headers data matrix! design design matrix similar object (optional). mode design formula, must specify either 'isolated' 'integrated'. Isolated means limma determines results level using data level. Integrated means limma determines results levels using full dataset (levels). spline_params Parameters spline functions (optional). Must contain named elements spline_type, must contain either string \"n\" natural cubic splines, \"b\", B-splines, named element degree case B-splines, must contain integer, named element dof, specifying degree freedom, containing integer required natural B-splines. padjust_method Method p-value adjustment, one \"none\", \"BH\", \"\", \"holm\", \"bonferroni\", \"hochberg\", \"hommel\". Defaults \"BH\" (Benjamini-Hochberg).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"SplineOmics object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_toc.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Table of Contents β create_toc","title":"Create Table of Contents β create_toc","text":"Creates HTML content Table Contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_toc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Table of Contents β create_toc","text":"","code":"create_toc()"},{"path":"https://csbg.github.io/SplineOmics/reference/create_toc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Table of Contents β create_toc","text":"string containing HTML Table Contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Volcano Plot β create_volcano_plot","title":"Create a Volcano Plot β create_volcano_plot","text":"function creates volcano plot limma top table, plotting log fold changes negative log10 adjusted p-values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Volcano Plot β create_volcano_plot","text":"","code":"create_volcano_plot(top_table, adj_pthresh, compared_levels)"},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Volcano Plot β create_volcano_plot","text":"top_table data frame limma containing 'logFC' 'adj.P.Val' columns. adj_pthresh numeric value adjusted p-value threshold. compared_levels character vector length 2 specifying compared levels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Volcano Plot β create_volcano_plot","text":"ggplot object representing volcano plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Database File to TERM2GENE List β dbs_to_term2genes","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"Reads specified .tsv file containing information databases, gene sets, genes. file three columns: 'DB' database names, Geneset' gene set identifiers, 'Gene' gene names. function organizes information nested list. top-level element corresponds unique database, within , gene sets map lists associated genes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"","code":"dbs_to_term2genes(databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"databases dataframe, containing three columns DB, Geneset, gene. dataframe contains databases downloaded Enrichr SplineOmics package function: download_enrichr_databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"nested list first level names corresponds database names ('DB'), second level gene sets ('Geneset'), innermost lists contain gene names ('Gene') associated gene set.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/define_html_styles.html","id":null,"dir":"Reference","previous_headings":"","what":"Define HTML Styles β define_html_styles","title":"Define HTML Styles β define_html_styles","text":"Defines CSS styles section headers Table Contents (TOC) entries used GSEA report generation.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/define_html_styles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define HTML Styles β define_html_styles","text":"","code":"define_html_styles()"},{"path":"https://csbg.github.io/SplineOmics/reference/define_html_styles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define HTML Styles β define_html_styles","text":"list containing styles section headers TOC entries.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Design Matrix for Splines β design2design_matrix","title":"Create Design Matrix for Splines β design2design_matrix","text":"function generates design matrix using spline parameters metadata. accommodates B-splines natural cubic splines based provided spline type parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Design Matrix for Splines β design2design_matrix","text":"","code":"design2design_matrix(meta, spline_params, level_index, design)"},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Design Matrix for Splines β design2design_matrix","text":"meta dataframe containing metadata, including time column. spline_params list containing spline parameters. list can include `dof` (degrees freedom), `knots`, `bknots` (boundary knots), `spline_type`, `degree`. level_index integer representing current level index design matrix generated. design character string representing design formula used generating model matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Design Matrix for Splines β design2design_matrix","text":"design matrix constructed using specified spline parameters design formula.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads specified Enrichr databases from the web β download_enrichr_databases","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"function downloads gene sets specified Enrichr databases saves specified output directory .tsv file. file named timestamp ensure uniqueness.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"","code":"download_enrichr_databases( gene_set_lib, output_dir = here::here(), filename = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"gene_set_lib character vector database names download Enrichr. output_dir character string specifying output directory .tsv file saved. Defaults current working directory. filename Name output file (file extension. Due commas present terms, .tsv recommendet). ommited, file named all_databases_timestamp.tsv.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"function return value saves .tsv file specified directory containing gene sets specified Enrichr databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":null,"dir":"Reference","previous_headings":"","what":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"function takes dataframe input returns base64 encoded CSV object. encoded object can embedded HTML document directly, button download file without pointing local file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"","code":"encode_df_to_base64(df, report_type = NA)"},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"df dataframe encoded. report_type (Optional) string specifying report generation function called. Generates different Excel sheet names based report_type.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"character string containing base64 encoded CSV data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Enrichr Gene Sets β enrichr_get_genesets","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"function downloads gene sets specified Enrichr databases. returns list element list corresponding database, element containing vector human gene symbols gene set.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"","code":"enrichr_get_genesets(databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"databases character vector database names download Enrichr.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"named list gene sets specified Enrichr databases. database represented list, gene set names list names vectors human gene symbols list elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ensure_clusterProfiler.html","id":null,"dir":"Reference","previous_headings":"","what":"Ensure 'clusterProfiler' is installed and loaded β ensure_clusterProfiler","title":"Ensure 'clusterProfiler' is installed and loaded β ensure_clusterProfiler","text":"function checks 'clusterProfiler' package installed. , prompts user choose whether install automatically, install manually, cancel operation. installed, package loaded use.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ensure_clusterProfiler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ensure 'clusterProfiler' is installed and loaded β ensure_clusterProfiler","text":"","code":"ensure_clusterProfiler()"},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates exploratory plots of the data matrix such as PCA β explore_data","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"function takes data matrix, checks validity, generates list exploratory plots including density plots, boxplots, PCA plots, MDS plots, variance explained plots, violin plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"","code":"explore_data(splineomics, report_dir = here::here(), report = TRUE)"},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"splineomics SplineOmics object, containing data, meta, condition, report_info, meta_batch_column, meta_batch2_column; report_dir non-empty string specifying report directory. report Boolean TRUE FALSE value, specifying report generated . report generated per default, plots plot objects inside R desired, argument can set FALSE.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"list ggplot objects representing various exploratory plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"function takes dataframe identifies rectangular quadratic area containing numeric data, starting first occurrence 6x6 block numeric values. extracts area matrix, ensuring row contains numeric values. Rows NA values removed resulting matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"","code":"extract_data(data, feature_name_columns = NA, user_prompt = TRUE)"},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"data dataframe loaded tabular file, potentially containing rectangular quadratic area numeric data amidst values. feature_name_columns (Optional) character vector, specifying columns dataframe data, used construct feature names. ommited, feature names just numbers (stored characters) starting 1 (1, 2, 3, etc.) user_prompt Boolean specifying whether user prompt correct format input data shown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"numeric matrix row headers appropriate column names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten Spline Configurations β flatten_spline_configs","title":"Flatten Spline Configurations β flatten_spline_configs","text":"Flattens formats spline configurations list formatted strings.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten Spline Configurations β flatten_spline_configs","text":"","code":"flatten_spline_configs(spline_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten Spline Configurations β flatten_spline_configs","text":"spline_configs list spline configuration objects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flatten Spline Configurations β flatten_spline_configs","text":"list formatted strings representing spline configuration.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Format text β format_text","title":"Format text β format_text","text":"function takes character vector `text` splits individual characters. iterates characters builds lines exceeding specified character limit (default 70). Newlines inserted lines using `
` tag, suitable HTML display.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format text β format_text","text":"","code":"format_text(text)"},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format text β format_text","text":"text character vector formatted.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format text β format_text","text":"character vector formatted text containing line breaks.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Composite Spline Plots β gen_composite_spline_plots","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"Creates composite spline plots significant non-significant features across multiple levels within condition. One half one condition comparison HTML (composite spline plots one 'condition' inside one condition comparison)","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"","code":"gen_composite_spline_plots( internal_combos, datas, metas, spline_test_configs, time_unit_label )"},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"internal_combos list containing combinations top tables. datas list matrices. metas list metadata corresponding data matrices. spline_test_configs configuration object spline tests. time_unit_label character string specifying time unit label plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"list containing composite spline plots lengths.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Hit Comparison Plots β gen_hitcomp_plots","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"Generates Venn heatmap barplot given combination pair top tables.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"","code":"gen_hitcomp_plots(combo_pair)"},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"combo_pair list containing two combinations top tables.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"list containing Venn heatmap plot, number hits divided 16, barplot, length indicator barplot.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/generate_and_write_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate and Write HTML Report β generate_and_write_html","title":"Generate and Write HTML Report β generate_and_write_html","text":"function generates HTML report inserting table contents, embedding necessary JavaScript files, writing final HTML content specified output file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_and_write_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate and Write HTML Report β generate_and_write_html","text":"","code":"generate_and_write_html(toc, html_content, report_info, output_file_path)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_and_write_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate and Write HTML Report β generate_and_write_html","text":"toc string containing table contents HTML format. html_content string containing main HTML content placeholder table contents. report_info list containing report information `contact_info` `analyst_name`. output_file_path string specifying path final HTML file written.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"Creates p-value histograms volcano plots condition average difference conditions. function used internally `create_limma_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"","code":"generate_avrg_diff_plots(avrg_diff_conditions, adj_pthresh)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"avrg_diff_conditions list top tables LIMMA analysis representing average difference conditions. adj_pthresh numeric value specifying adjusted p-value threshold significance.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"list containing plots sizes, well section header information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate exploratory plots β generate_explore_plots","title":"Generate exploratory plots β generate_explore_plots","text":"function generates various exploratory plots including density plots, box plots, violin plots, PCA plots, correlation heatmaps based provided data metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate exploratory plots β generate_explore_plots","text":"","code":"generate_explore_plots(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate exploratory plots β generate_explore_plots","text":"data data frame matrix containing data plotted. meta data frame containing metadata associated data. condition string specifying column metadata contains condition grouping variable.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate exploratory plots β generate_explore_plots","text":"list containing two elements: plots list ggplot objects representing generated plots. plots_sizes vector numeric values indicating sizes corresponding plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"Creates p-value histograms interaction condition interaction condition time. function used internally `create_limma_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"","code":"generate_interaction_plots(interaction_condition_time, adj_pthresh)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"interaction_condition_time list top tables LIMMA analysis representing interaction effects condition time. adj_pthresh numeric value specifying adjusted p-value threshold significance.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"list containing plots sizes, well section header information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":null,"dir":"Reference","previous_headings":"","what":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"Generates HTML report provided plots, spline parameters, report information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"","code":"generate_report_html( plots, plots_sizes, report_info, limma_result_2_and_3_plots = NULL, data = NULL, meta = NA, topTables = NA, enrichr_format = NA, level_headers_info = NA, spline_params = NA, adj_pthresholds = NA, adj_pthresh_avrg_diff_conditions = NA, adj_pthresh_interaction_condition_time = NA, report_type = \"explore_data\", feature_name_columns = NA, mode = NA, filename = \"report\", timestamp = format(Sys.time(), \"%d_%m_%Y-%H_%M_%S\"), report_dir = here::here() )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"plots list ggplot2 plot objects. plots_sizes list integers specifying size plot. report_info named list containing report information. limma_result_2_and_3_plots List containing list lists plots pairwise comparisons condition terms average spline diff interaction condition time, another list lists respective names plot stored. data dataframe list dataframes, containing data directly embedded HTML report downloading. meta dataframe, containing metadata directly embedded HTML report downloading. topTables List limma topTables enrichr_format List, containing two lists: gene list list background genes. level_headers_info list header information level. spline_params list spline parameters, dof type. adj_pthresholds Numeric vector values adj.p.tresholds level. adj_pthresh_avrg_diff_conditions Float, cluster_hits() adj_pthresh_interaction_condition_time Float, cluster_hits() report_type character string specifying report type ('screen_limma_hyperparams' 'cluster_hits'). feature_name_columns Character vector column names annotation information, columns containing gene names. column names used put info HTML reports descriptions individual spline plots created. descriptions can made several column values, specific columns stated HTML report top (e.g gene_uniprotID). mode character string specifying mode ('isolated' 'integrated'). filename character string specifying filename report. timestamp timestamp include report filename. report_dir character string specifying report directory.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Reports β generate_reports","title":"Generate Reports β generate_reports","text":"Builds HTML reports pairwise hyperparameter combination comparisons.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Reports β generate_reports","text":"","code":"generate_reports(combo_pair_plots, report_info, report_dir, timestamp)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Reports β generate_reports","text":"combo_pair_plots list plots pair combinations. report_info object containing report information. report_dir non-empty string specifying report directory. timestamp timestamp include reports.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Reports β generate_reports","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Reports Metadata β generate_reports_meta","title":"Generate Reports Metadata β generate_reports_meta","text":"Generates metadata table LIMMA hyperparameter screen reports saves HTML file custom styling.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Reports Metadata β generate_reports_meta","text":"","code":"generate_reports_meta( datas_descr, designs, modes, spline_test_configs, report_dir, timestamp )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Reports Metadata β generate_reports_meta","text":"datas_descr description object data. designs list design matrices. modes character vector containing 'isolated' 'integrated'. spline_test_configs configuration object spline tests. report_dir non-empty string specifying report directory. timestamp timestamp include report filename.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Reports Metadata β generate_reports_meta","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Section Content β generate_section_content","title":"Generate Section Content β generate_section_content","text":"Generates HTML content section, including headers enrichment results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Section Content β generate_section_content","text":"","code":"generate_section_content( section_info, index, toc, html_content, section_header_style, toc_style )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Section Content β generate_section_content","text":"section_info list containing information section. index index current section. toc current state Table Contents. html_content current state HTML content. section_header_style CSS style section headers. toc_style CSS style TOC entries.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Section Content β generate_section_content","text":"list updated HTML content TOC.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"function generates spline comparison plots pairwise combinations conditions metadata. condition pair, compares time effects two conditions, plots data points, overlays fitted spline curves. function generates plots adjusted p-values average difference conditions interaction condition time specified thresholds.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"","code":"generate_spline_comparisons( splineomics, all_levels_clustering, data, meta, condition, plot_info, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"splineomics list containing splineomics results, including time effects, average difference conditions, interaction condition time. all_levels_clustering list containing X matrices condition, used spline fitting. data data matrix containing measurements. meta metadata associated measurements, includes condition. condition Column name meta contains levels experiment. plot_info list containing plotting information time unit axis labels. adj_pthresh_avrg_diff_conditions adjusted p-value threshold average difference conditions. adj_pthresh_interaction adjusted p-value threshold interaction condition time. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"list lists containing comparison plots feature names condition pair.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Plots for Time Effect β generate_time_effect_plots","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"Creates p-value histograms time effect LIMMA analysis. function used internally `create_limma_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"","code":"generate_time_effect_plots(time_effect, adj_pthresh)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"time_effect list top tables LIMMA analysis representing time effects. adj_pthresh numeric value specifying adjusted p-value threshold significance.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"list containing plots sizes, well section header information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Curve Values Based on Top Table Filter β get_curve_values","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"function filters entries given top table based adjusted p-value threshold, performs spline interpolation using specified degrees freedom, calculates curve values selected entries predefined time points. function internal exported.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"","code":"get_curve_values(top_table, level, meta, condition, spline_params, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"top_table data frame containing data column adjusted p-values expression averages indicate number degrees freedom. level specific level condition filter metadata. meta Metadata containing time points conditions. condition name condition column metadata filter . spline_params list spline parameters analysis. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"list containing two elements: `curve_values`, data frame curve values filtered entry, `smooth_timepoints`, time points curves evaluated.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Plot Explanations β get_explore_plots_explanations","title":"Get Plot Explanations β get_explore_plots_explanations","text":"function returns vector text explanations various types plots. explanations used HTML reports describe plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Plot Explanations β get_explore_plots_explanations","text":"","code":"get_explore_plots_explanations()"},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Plot Explanations β get_explore_plots_explanations","text":"character vector containing explanations different plot types.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Plot Explanations β get_explore_plots_explanations","text":"explanations cover variety plots, including density plots, boxplots, violin plots, mean time correlation plots, lag-1 differences plots, first lag autocorrelation plots, coefficient variation (CV) plots, PCA plots, PCA variance explained plots, MDS plots, correlation heatmaps. explanation provides insights plot shows interpret .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Header Section β get_header_section","title":"Get Header Section β get_header_section","text":"Generates HTML header section report, including title, header text, logo. section also includes styling table HTML elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Header Section β get_header_section","text":"","code":"get_header_section(title, header_text, report_type, feature_names_formula)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Header Section β get_header_section","text":"title string specifying title HTML document. header_text string specifying text displayed header report. report_type character specifying type HTML report. feature_names_formula String describing columns annotation info, gene uniprotID, used construct description individual spline plots. placed beginning output HTML reports.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Header Section β get_header_section","text":"string containing HTML header section.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Header Section β get_header_section","text":"function checks `DEVTOOLS_LOAD` environment variable determine path logo image. logo image converted base64 data URI included HTML. header section includes styles tables, table cells, header elements ensure proper formatting alignment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Hit Indices for a Specific Level β get_level_hit_indices","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"function retrieves unique feature indices list -level top tables specified level, based adjusted p-value thresholds.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"","code":"get_level_hit_indices(between_level_top_tables, level, adj_pthresholds)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"between_level_top_tables list data frames containing -level top tables. level string specifying level search within names data frames. adj_pthresholds numeric vector adjusted p-value thresholds data frame `between_level_top_tables`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"vector unique feature indices meet adjusted p-value threshold criteria specified level.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"function iterates data frame `between_level_top_tables`. data frame whose name contains specified level (case insensitive), identifies rows adjusted p-value corresponding threshold. function extracts feature indices rows compiles unique list indices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate LIMMA Combination Results β get_limma_combos_results","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"Computes results various combinations data, design matrices, spline configurations using LIMMA method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"","code":"get_limma_combos_results( datas, rna_seq_datas, metas, designs, modes, condition, spline_test_configs, feature_names, adj_pthresholds, padjust_method )"},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"datas list matrices. rna_seq_datas list RNA-seq data objects, voom object derived limma::voom function. metas list metadata corresponding data matrices. designs list design matrices. modes character vector containing 'isolated' 'integrated'. condition single character string specifying condition. spline_test_configs configuration object spline tests. feature_names character vector feature names. adj_pthresholds numeric vector elements > 0 < 1. padjust_method single character string specifying p-adjustment method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"list results combination data, design, spline configuration.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Spline Parameters Info β get_spline_params_info","title":"Get Spline Parameters Info β get_spline_params_info","text":"function retrieves spline parameters information given index. ensures spline parameters valid constructs HTML string describing spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Spline Parameters Info β get_spline_params_info","text":"","code":"get_spline_params_info(spline_params, j)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Spline Parameters Info β get_spline_params_info","text":"spline_params list containing spline parameters. list include elements: `spline_type`, `degree`, `dof`, `knots`, `bknots`. j integer specifying index spline parameters retrieve.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Spline Parameters Info β get_spline_params_info","text":"character string containing HTML-formatted information spline parameters specified index.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Spline Parameters Info β get_spline_params_info","text":"function checks spline parameters `NULL` length greater equal specified index `j`. parameter invalid missing, sets parameter `NA`. constructs HTML string describing spline parameters, including spline type, degree, degrees freedom (DoF), knots, boundary knots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Data to Hit Comparison Object β hc_add","title":"Add Data to Hit Comparison Object β hc_add","text":"Adds new entry hit comparison object specified condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Data to Hit Comparison Object β hc_add","text":"","code":"hc_add(hc_obj, top_table, params_id, condition = 1, threshold = 0.05)"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Data to Hit Comparison Object β hc_add","text":"hc_obj object class \"hitcomp\". top_table dataframe containing top table data. params_id character string identifying parameters (max length 70). condition integer (1 2) specifying condition data belongs. threshold numeric value specifying adjusted p-value threshold.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Data to Hit Comparison Object β hc_add","text":"updated hit comparison object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Barplot for Hit Comparison Object β hc_barplot","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"Creates barplot visualize number significant features parameter set hit comparison object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"","code":"hc_barplot(hc_obj)"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"hc_obj object class \"hitcomp\" containing hit data two conditions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"ggplot2 object representing barplot.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":null,"dir":"Reference","previous_headings":"","what":"Create New Hit Comparison Object β hc_new","title":"Create New Hit Comparison Object β hc_new","text":"Creates new hit comparison object specified condition names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create New Hit Comparison Object β hc_new","text":"","code":"hc_new(cond1name = \"Condition 1\", cond2name = \"Condition 2\")"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create New Hit Comparison Object β hc_new","text":"cond1name character string first condition name (max length 25). cond2name character string second condition name (max length 25).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create New Hit Comparison Object β hc_new","text":"object class \"hitcomp\" containing empty data lists condition names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Venn Heatmap β hc_vennheatmap","title":"Generate Venn Heatmap β hc_vennheatmap","text":"Creates Venn heatmap visualize overlap hits two conditions stored hit comparison object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Venn Heatmap β hc_vennheatmap","text":"","code":"hc_vennheatmap(hc_obj)"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Venn Heatmap β hc_vennheatmap","text":"hc_obj object class \"hitcomp\" containing hit data two conditions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Venn Heatmap β hc_vennheatmap","text":"list containing Venn heatmap plot number hits.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Clustering of Curve Values β hierarchical_clustering","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"Performs hierarchical clustering given curve values. function adjusts provided top_table cluster assignments.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"","code":"hierarchical_clustering(curve_values, k, smooth_timepoints, top_table)"},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"curve_values matrix data frame curve values cluster. k number clusters use. smooth_timepoints Numeric vector time points corresponding columns curve_values. top_table Data frame updated cluster assignments.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"list containing clustering results modified top_table.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"function iterates list tables checks table 300 rows. table found, prompts user proceed stop.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"","code":"huge_table_user_prompter(tables)"},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"tables list data frames.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"NULL. function used side effects (prompting user potentially stopping script).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if Not All Values are NA β is_not_na","title":"Check if Not All Values are NA β is_not_na","text":"Determines given atomic vector contains least one non-NA value.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if Not All Values are NA β is_not_na","text":"","code":"is_not_na(x)"},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if Not All Values are NA β is_not_na","text":"x atomic vector object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if Not All Values are NA β is_not_na","text":"TRUE vector contains least one non-NA value object atomic; FALSE otherwise.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Clustering Report β make_clustering_report","title":"Make Clustering Report β make_clustering_report","text":"Generates detailed clustering report including heatmaps, dendrograms, curve plots, consensus shapes level within condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Clustering Report β make_clustering_report","text":"","code":"make_clustering_report( all_levels_clustering, condition, data, meta, annotation, genes, spline_params, adj_pthresholds, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction_condition_time, report_dir, mode, report_info, design, meta_batch_column, meta_batch2_column, plot_info, plot_options, feature_name_columns, spline_comp_plots, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Clustering Report β make_clustering_report","text":"all_levels_clustering list containing clustering results level within condition. condition character string specifying condition. data matrix data values. meta dataframe containing metadata. annotation Dataframe containig annotation info features, gene uniprotID, example. genes Character vector containing genes features. spline_params list spline parameters analysis. adj_pthresholds Numeric vector, containing float < 1 > 0 value. one float every level, adj. p-value threshold. adj_pthresh_avrg_diff_conditions Float adj_pthresh_interaction_condition_time Float report_dir character string specifying report directory. mode character string specifying mode ('isolated' 'integrated'). report_info object containing report information. design string representing limma design formula meta_batch_column character string specifying meta batch column. meta_batch2_column character string specifying second meta batch column. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). plot_options List specific fields (cluster_heatmap_columns = Bool) allow customization plotting behavior. feature_name_columns Character vector containing column names annotation info describe features. argument used specify HTML report exactly feature names displayed individual spline plot created. Use vector used create row headers data matrix! spline_comp_plots List containing list lists plots pairwise comparisons condition terms average spline diff interaction condition time, another list lists respective names plot stored. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Clustering Report β make_clustering_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Correlation Heatmaps β make_correlation_heatmaps","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"function generates correlation heatmaps using Spearman correlation given data matrix. creates combined heatmap levels individual heatmaps level specified condition column metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"","code":"make_correlation_heatmaps(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"data numeric matrix containing data. meta dataframe containing metadata. condition column name metadata dataframe contains factor levels generating individual heatmaps.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"list `ComplexHeatmap` heatmap objects representing correlation heatmaps.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Density Plot β make_density_plots","title":"Generate Density Plot β make_density_plots","text":"function generates density plot given data matrix. density plot shows distribution values data matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Density Plot β make_density_plots","text":"","code":"make_density_plots(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Density Plot β make_density_plots","text":"data numeric matrix containing data. meta dataframe containing column meta data data condition name factor column meta experiment","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Density Plot β make_density_plots","text":"ggplot object representing density plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Enrich Dotplot β make_enrich_dotplot","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"Make enriched dotplot visualization.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"","code":"make_enrich_dotplot(enrichments_list, databases, title = \"Title\")"},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"enrichments_list list enrichments containing data frames different databases. databases character vector specifying databases included. title character string specifying title dotplot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"list containing: p ggplot object representing dotplot. dotplot_nrows integer specifying number rows dotplot. full_enrich_results data frame containing full enrichments results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate MDS Plot β make_mds_plot","title":"Generate MDS Plot β make_mds_plot","text":"function generates multidimensional scaling (MDS) plot given data matrix. MDS plot visualizes similarities dissimilarities samples data matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate MDS Plot β make_mds_plot","text":"","code":"make_mds_plot(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate MDS Plot β make_mds_plot","text":"data numeric matrix containing data. meta dataframe, containign meta information data. condition column meta dataframe containign levels separate experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate MDS Plot β make_mds_plot","text":"ggplot object representing MDS plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"function generates PCA plot data matrix, dynamically coloring points based levels specified factor metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"","code":"make_pca_plot(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"data numeric matrix containing data. meta dataframe containing metadata. condition column name metadata dataframe contains factor levels coloring PCA plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"ggplot object representing PCA plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_scatter_plot_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","title":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","text":"Generate scatter plot report rach feature embedded base64 images","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_scatter_plot_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","text":"","code":"make_scatter_plot_html( data, meta, output_file = \"scatter_plots_base64.html\", meta_replicate_column = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/make_scatter_plot_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","text":"data matrix features rows samples columns. Row names feature names. meta data frame meta information. Must contain numeric column \"Time\". output_file name HTML output file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Violin Box Plot β make_violin_box_plots","title":"Generate Violin Box Plot β make_violin_box_plots","text":"function generates violin plot given data matrix. violin plot shows distribution values data matrix across different variables, variable's distribution displayed separate violin.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Violin Box Plot β make_violin_box_plots","text":"","code":"make_violin_box_plots(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Violin Box Plot β make_violin_box_plots","text":"data numeric matrix containing data. meta dataframe containing column meta data data condition name factor column meta experiment","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Violin Box Plot β make_violin_box_plots","text":"ggplot object representing violin plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"function manages GSEA analysis specific level. extracts genes associated clustered hits, removes rows `NA` values, runs GSEA analysis using `create_gsea_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"","code":"manage_gsea_level( clustered_hits, level_name, databases, clusterProfiler_params, universe )"},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"clustered_hits dataframe containing clustered hits specific level. must include column named `feature` extract genes. level_name character string representing name level. databases list databases gene set enrichment analysis. clusterProfiler_params Additional parameters GSEA analysis, default NA. include adj_p_value, pAdjustMethod, etc (see clusterProfiler documentation). universe Enrichment background data, default NULL.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"result `create_gsea_report` function, typically includes various plots enrichment results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"internal function checks whether valid treatment timepoints labels `plot_info` list. found, adds dashed vertical lines corresponding x-axis values plot. treatment timepoints labels can either named lists (multiple levels) unnamed single elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"","code":"maybe_add_dashed_lines( p, plot_info, level, y_pos = 1, horizontal_labels = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"p ggplot object. plot dashed lines labels added. plot_info list containing treatment timepoints treatment labels. Treatment timepoints labels can either unnamed elements named lists element corresponds different `level`. level character string. Used extract treatment timepoints labels stored named lists. y_pos numeric value specifying y-axis position text labels placed. Defaults 1. horizontal_labels Boolean flag indicating whether vertical label (default) horizontal label.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"list containing: - `p`: ggplot object possibly added dashed lines labels. - `treatment_colors`: named vector colors used treatment labels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"function merges annotation information `top_table` non-logical element list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"","code":"merge_annotation_all_levels_clustering( all_levels_clustering, annotation = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"all_levels_clustering list element contains `top_table` dataframe `feature_nr` column. elements may logical values. annotation dataframe containing annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"list updated `top_table` dataframes containing merged annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"function merges annotation information single `top_table` dataframe based `feature_nr` column.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"","code":"merge_top_table_with_annotation(top_table, annotation)"},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"top_table dataframe containing `top_table` `feature_nr` column. annotation dataframe containing annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"dataframe updated `top_table` containing merged annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify limma Top Table β modify_limma_top_table","title":"Modify limma Top Table β modify_limma_top_table","text":"Modifies limma top table include feature indices names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify limma Top Table β modify_limma_top_table","text":"","code":"modify_limma_top_table(top_table, feature_names)"},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify limma Top Table β modify_limma_top_table","text":"top_table dataframe containing top table results limma feature_names character vector feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Modify limma Top Table β modify_limma_top_table","text":"tibble feature indices names included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize Curve Values β normalize_curves","title":"Normalize Curve Values β normalize_curves","text":"function normalizes row data frame matrix curve values. Normalization performed row's values range 0 (corresponding minimum value row) 1 (corresponding maximum value row).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize Curve Values β normalize_curves","text":"","code":"normalize_curves(curve_values)"},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize Curve Values β normalize_curves","text":"curve_values data frame matrix curve values row represents curve column time point.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize Curve Values β normalize_curves","text":"data frame matrix dimensions input, row normalized.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Open template for SplineOmics analysis for quick setup β open_template","title":"Open template for SplineOmics analysis for quick setup β open_template","text":"function opens `template.Rmd` file RStudio interactive use. template file provides structure users quickly set personal analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Open template for SplineOmics analysis for quick setup β open_template","text":"","code":"open_template()"},{"path":"https://csbg.github.io/SplineOmics/reference/open_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Open template for SplineOmics analysis for quick setup β open_template","text":"successful, opens `template.Rmd` file RStudio user interact . `rstudioapi` installed available, template file found, error thrown corresponding message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_tutorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","title":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","text":"function opens `tutorial.Rmd` file RStudio interactive use. Users can run code chunk step step.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_tutorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","text":"","code":"open_tutorial()"},{"path":"https://csbg.github.io/SplineOmics/reference/open_tutorial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","text":"successful, opens `tutorial.Rmd` file RStudio user interact . `rstudioapi` installed available, tutorial file found, error thrown corresponding message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Clustering β perform_clustering","title":"Perform Clustering β perform_clustering","text":"Performs clustering top tables using specified p-values clusters level within condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Clustering β perform_clustering","text":"","code":"perform_clustering(top_tables, clusters, meta, condition, spline_params, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Clustering β perform_clustering","text":"top_tables list top tables limma analysis. clusters list specifying clusters. meta dataframe containing metadata. condition character string specifying condition. spline_params list spline parameters analysis. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Clustering β perform_clustering","text":"list clustering results level within condition.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Plot to Base64 β plot2base64","title":"Convert Plot to Base64 β plot2base64","text":"Converts ggplot2 plot Base64-encoded PNG image returns HTML img tag embedding report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Plot to Base64 β plot2base64","text":"","code":"plot2base64( plot, height, width = 7, base_height_per_row = 2.5, units = \"in\", html_img_width = \"100%\" )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Plot to Base64 β plot2base64","text":"plot ggplot2 plot object. height integer specifying height plot correct representation HTML. width numeric value specifying width plot inches. base_height_per_row numeric value specifying base height per row inches. units character string specifying units width height. html_img_width character string specifying width image HTML.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Plot to Base64 β plot2base64","text":"character string containing HTML img tag Base64-encoded plot.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot All Mean Splines β plot_all_mean_splines","title":"Plot All Mean Splines β plot_all_mean_splines","text":"Generates plot average curves cluster, showing min-max normalized intensities time.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot All Mean Splines β plot_all_mean_splines","text":"","code":"plot_all_mean_splines(curve_values, plot_info, level)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot All Mean Splines β plot_all_mean_splines","text":"curve_values dataframe containing curve values cluster assignments. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). level One unique values meta condition column. factor separates experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot All Mean Splines β plot_all_mean_splines","text":"ggplot object representing average curves cluster.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Consensus Shapes β plot_cluster_mean_splines","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"Generates composite plots single consensus shapes cluster curve values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"","code":"plot_cluster_mean_splines(curve_values, plot_info, level)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"curve_values dataframe containing curve values cluster assignments. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"list containing plot every cluster","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Composite Splines β plot_composite_splines","title":"Plot Composite Splines β plot_composite_splines","text":"Generates composite spline plots significant non-significant features based specified indices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Composite Splines β plot_composite_splines","text":"","code":"plot_composite_splines( data, meta, spline_test_configs, top_table, top_table_name, indices, type, time_unit_label )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Composite Splines β plot_composite_splines","text":"data matrix data values. meta dataframe containing metadata. spline_test_configs configuration object spline tests. top_table dataframe containing top table results. top_table_name character string specifying name top table. indices vector indices specifying features plot. type character string specifying type features ('significant' 'not_significant'). time_unit_label string shown plots unit time, min hours.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Composite Splines β plot_composite_splines","text":"list containing composite plot length plots generated, FALSE otherwise.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of Variation (CV) Plot β plot_cv","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"function takes data frame time series data (rows features columns samples), meta table sample information including time points conditions, computes coefficient variation (CV) feature condition level, plots distribution CVs.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"","code":"plot_cv(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\" condition column. condition name column meta table contains condition information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"list ggplot2 objects, showing distribution CVs one condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Dendrogram β plot_dendrogram","title":"Plot Dendrogram β plot_dendrogram","text":"Generates dendrogram plot hierarchical clustering results, colored clusters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Dendrogram β plot_dendrogram","text":"","code":"plot_dendrogram(hc, clusters, k)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Dendrogram β plot_dendrogram","text":"hc hierarchical clustering object. clusters numeric vector, specifying cluster hit . Index 1 cluster hit nr. 1, index 2 hit nr. 2, etc. k integer specifying number clusters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Dendrogram β plot_dendrogram","text":"ggplot object representing dendrogram.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"function takes data frame time series data (rows features columns samples), meta table sample information including time points conditions, computes first lag autocorrelation feature condition level, plots distribution autocorrelation coefficients.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"","code":"plot_first_lag_autocorrelation(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\" condition column. condition name column meta table contains condition information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"list ggplot2 objects, showing distribution first lag autocorrelation coefficients one condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Heatmap β plot_heatmap","title":"Plot Heatmap β plot_heatmap","text":"Generates heatmaps level within condition, showing z-scores log2 intensity values, split clusters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Heatmap β plot_heatmap","text":"","code":"plot_heatmap( datas, meta, mode, condition, all_levels_clustering, time_unit_label, cluster_heatmap_columns )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Heatmap β plot_heatmap","text":"datas matrix data values. meta dataframe containing metadata. mode character vector length 1, specifying type limma design formula (integrated formulas interaction effects levels, isolated formulas level analysed isolation (interaction effects)) condition character string specifying condition. all_levels_clustering list containing clustering results level within condition. time_unit_label character string specifying time unit label. cluster_heatmap_columns Boolean specifying wether cluster columns heatmap .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Heatmap β plot_heatmap","text":"list ComplexHeatmap heatmap objects level.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":null,"dir":"Reference","previous_headings":"","what":"Lag-1 Differences Plot β plot_lag1_differences","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"function takes data frame time series data (rows features columns samples), meta table sample information including time points conditions, computes lag-1 differences feature condition level, plots distribution differences.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"","code":"plot_lag1_differences(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\" condition column. condition name column meta table contains condition information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"list ggplot2 objects, showing distribution lag-1 differences one condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot limma Combination Results β plot_limma_combos_results","title":"Plot limma Combination Results β plot_limma_combos_results","text":"Generates plots pairwise comparisons hyperparameter combinations using limma results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot limma Combination Results β plot_limma_combos_results","text":"","code":"plot_limma_combos_results( top_tables_combos, datas, metas, condition, spline_test_configs, meta_batch_column, meta_batch2_column, time_unit = time_unit )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot limma Combination Results β plot_limma_combos_results","text":"top_tables_combos list top tables combination. datas list matrices. metas list metadata corresponding data matrices. condition Meta column name contains levels. spline_test_configs configuration object spline tests. meta_batch_column character string specifying meta batch column. meta_batch2_column character string specifying second meta batch column. time_unit single character, s, m, h, d, specifying time_unit used plots (s = seconds, m = minutes, h = hours, d = days). single character converted string little bit verbose, sec square brackets s.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot limma Combination Results β plot_limma_combos_results","text":"list results including hit comparison plots composite spline plots pair combinations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"function takes data frame time series data (rows features columns samples) meta table sample information including time points, computes correlation feature time, plots distribution correlations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"","code":"plot_mean_correlation_with_time(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\". condition column meta dataframe containign levels separate experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"ggplot2 object showing distribution mean correlations time. @importFrom rlang .data","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Single and Mean Splines β plot_single_and_mean_splines","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"Generates plot showing individual time series shapes consensus (mean) shape.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"","code":"plot_single_and_mean_splines(time_series_data, title, plot_info, level)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"time_series_data dataframe matrix time series data. title character string specifying title plot. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"ggplot object representing single consensus shapes.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Create spline comparison plots for two conditions β plot_spline_comparisons","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"function generates comparison plots spline fits two conditions time. compares time effects two conditions, plots data points, overlays fitted spline curves. function checks adjusted p-values average difference conditions interaction condition time specified thresholds generating plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"","code":"plot_spline_comparisons( time_effect_1, condition_1, time_effect_2, condition_2, avrg_diff_conditions, interaction_condition_time, data, meta, condition, X_1, X_2, plot_info, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"time_effect_1 data frame containing time effects first condition. condition_1 name first condition. time_effect_2 data frame containing time effects second condition. condition_2 name second condition. avrg_diff_conditions data frame adjusted p-values average difference conditions. interaction_condition_time data frame adjusted p-values interaction condition time. data data matrix containing measurements. meta metadata associated measurements. condition Column name meta contains levels experiment. X_1 matrix spline basis values first condition. X_2 matrix spline basis values second condition. plot_info list containing plotting information time unit axis labels. adj_pthresh_avrg_diff_conditions adjusted p-value threshold average difference conditions. adj_pthresh_interaction adjusted p-value threshold interaction condition time. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"list containing: plots list ggplot2 plots comparing two conditions. feature_names list feature names plotted features.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Splines for Features Based on Top Table Information β plot_splines","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"function generates plots feature listed top table using spline interpolation fitted values. creates individual plots feature combines single composite plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"","code":"plot_splines( top_table, data, meta, X, time_unit_label, plot_info, adj_pthreshold, replicate_column, level, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"top_table dataframe containing indices names features, along statistical metrics intercepts spline coefficients. data matrix dataframe containing raw data values feature. meta dataframe containing metadata data, including time points. X limma design matrix defines experimental conditions. time_unit_label string shown plots unit time, min hours. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). adj_pthreshold Double > 0 < 1 specifying adj. p-val threshold. replicate_column String specifying column meta dataframe contains labels replicate measurents. given, argument NULL. level Unique value meta condition column, 'treatment' 'control'. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"list containing composite plot number rows used plot layout.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"function processes clustered hits element `all_levels_clustering`, formats gene names easy copy-pasting Enrichr, returns formatted gene lists string.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"","code":"prepare_gene_lists_for_enrichr(all_levels_clustering, genes)"},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"all_levels_clustering list element contains dataframe `clustered_hits` columns `feature` `cluster`. genes vector gene names corresponding feature indices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"character vector formatted gene lists cluster.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Plot Data β prepare_plot_data","title":"Prepare Plot Data β prepare_plot_data","text":"function prepares plot data visualization based enrichments lists specified databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Plot Data β prepare_plot_data","text":"","code":"prepare_plot_data(enrichments_list, databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Plot Data β prepare_plot_data","text":"enrichments_list list enrichments containing data frames different databases. databases character vector specifying databases included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare Plot Data β prepare_plot_data","text":"list containing two data frames: top_plot_data data frame containing prepared plot data visualization top combinations. full_enrich_results data frame containing full enrichments results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"`preprocess_rna_seq_data()` function performs essential preprocessing steps raw RNA-seq counts. includes creating `DGEList` object, normalizing counts using default TMM (Trimmed Mean M-values) normalization via `edgeR::calcNormFactors` function, applying `voom` transformation `limma` package obtain log-transformed counts per million (logCPM) associated precision weights. require different normalization method, can supply custom normalization function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"","code":"preprocess_rna_seq_data( raw_counts, meta, spline_params, design, normalize_func = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"raw_counts matrix raw RNA-seq counts (genes rows, samples columns). meta dataframe containing metadata data. spline_params Parameters spline functions (optional). Must contain named elements spline_type, must contain either string \"n\" natural cubic splines, \"b\", B-splines, named element degree case B-splines, must contain integer, named element dof, specifying degree freedom, containing integer required natural B-splines. design design formula limma analysis, '~ 1 + Phase*X + Reactor'. normalize_func optional normalization function. provided, function used normalize `DGEList` object. provided, TMM normalization (via `edgeR::calcNormFactors`) used default. Must take input y : y <- edgeR::DGEList(counts = raw_counts) output y normalized counts.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"`voom` object, includes log2-counts per million (logCPM) matrix observation-specific weights.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":null,"dir":"Reference","previous_headings":"","what":"Print function for SplineOmics objects β print.SplineOmics","title":"Print function for SplineOmics objects β print.SplineOmics","text":"function provides summary print SplineOmics object, showing relevant information number features, samples, metadata, RNA-seq data, annotation, spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print function for SplineOmics objects β print.SplineOmics","text":"","code":"# S3 method for class 'SplineOmics' print(x, ...)"},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print function for SplineOmics objects β print.SplineOmics","text":"x SplineOmics object created `create_splineomics` function. ... Additional arguments passed methods.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print function for SplineOmics objects β print.SplineOmics","text":"function return value. prints summary SplineOmics object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print function for SplineOmics objects β print.SplineOmics","text":"function automatically called SplineOmics object printed. provides concise overview object's contents attributes, including dimensions data, available metadata, relevant information annotations spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print_info_message.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Informational Message β print_info_message","title":"Print Informational Message β print_info_message","text":"function prints nicely formatted informational message green \"Info\" label.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print_info_message.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Informational Message β print_info_message","text":"","code":"print_info_message(message_prefix, report_dir)"},{"path":"https://csbg.github.io/SplineOmics/reference/print_info_message.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Informational Message β print_info_message","text":"message_prefix custom message prefix displayed success message. report_dir directory HTML reports located.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Combination β process_combo","title":"Process Combination β process_combo","text":"Processes single combination data, design, spline configuration, p-threshold generate LIMMA spline results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Combination β process_combo","text":"","code":"process_combo( data_index, design_index, spline_config_index, pthreshold, datas, rna_seq_datas, metas, designs, modes, condition, spline_test_configs, feature_names, padjust_method, ... )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Combination β process_combo","text":"data_index Index data datas list. design_index Index design designs list. spline_config_index Index spline configuration spline_test_configs list. pthreshold p-value threshold significance. datas list data matrices rna_seq_datas list RNA-seq data objects, voom object derived limma::voom function. metas list metadata corresponding data matrices. designs list design matrices. modes character vector containing 'isolated' 'integrated'. condition single character string specifying condition. spline_test_configs configuration object spline tests. feature_names character vector feature names. padjust_method single character string specifying p-adjustment method. ... Additional arguments.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Combination β process_combo","text":"list top tables LIMMA spline analysis.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Combination Pair β process_combo_pair","title":"Process Combination Pair β process_combo_pair","text":"Processes combination pair generate plots compile HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Combination Pair β process_combo_pair","text":"","code":"process_combo_pair( combo_pair, combo_pair_name, report_info, report_dir, timestamp )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Combination Pair β process_combo_pair","text":"combo_pair list containing hit comparison composite spline plots. combo_pair_name character string naming combination pair. report_info object containing report information. report_dir non-empty string specifying report directory. timestamp timestamp include report filename.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Combination Pair β process_combo_pair","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Configuration Column β process_config_column","title":"Process Configuration Column β process_config_column","text":"Processes configuration column based given mode number levels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Configuration Column β process_config_column","text":"","code":"process_config_column(config_column, index, num_levels, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Configuration Column β process_config_column","text":"config_column configuration column spline test configurations. index Index configuration process. num_levels Number unique levels metadata condition. mode character string specifying mode ('integrated' 'isolated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Configuration Column β process_config_column","text":"vector list processed configuration values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Enrichment Results β process_enrichment_results","title":"Process Enrichment Results β process_enrichment_results","text":"Process enrichment results visualization.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Enrichment Results β process_enrichment_results","text":"","code":"process_enrichment_results( all_db_results, enrichment_results, adjP_threshold, column_name, count_column_name, background = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Enrichment Results β process_enrichment_results","text":"all_db_results list data frames containing enrichment results databases. enrichment_results list data frames containing enrichment results individual databases. adjP_threshold threshold adjusted p-values. column_name name column store adjusted p-values. count_column_name name column store gene counts. background Logical indicating whether background ratios included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Enrichment Results β process_enrichment_results","text":"list data frames containing processed enrichment results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":null,"dir":"Reference","previous_headings":"","what":"Process and Encode Data Field for Report β process_field","title":"Process and Encode Data Field for Report β process_field","text":"function processes given field, encodes associated data base64, generates download link report. handles different types fields including data, meta, top tables, Enrichr formatted gene lists.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process and Encode Data Field for Report β process_field","text":"","code":"process_field( field, data, meta, topTables, report_info, encode_df_to_base64, enrichr_format )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process and Encode Data Field for Report β process_field","text":"field string specifying field process. data dataframe containing main data. meta dataframe containing meta information. topTables dataframe containing results differential expression analysis. report_info list containing additional report information. encode_df_to_base64 function encode dataframe base64. enrichr_format list formatted gene lists background gene list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process and Encode Data Field for Report β process_field","text":"string containing HTML link downloading processed field.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Level Cluster β process_level_cluster","title":"Process Level Cluster β process_level_cluster","text":"Processes clustering specific level within condition using provided top table spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Level Cluster β process_level_cluster","text":"","code":"process_level_cluster( top_table, cluster_size, level, meta, condition, spline_params, mode )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Level Cluster β process_level_cluster","text":"top_table dataframe containing top table results limma. cluster_size size clusters generate. level level within condition process. meta dataframe containing metadata. condition character string specifying condition. spline_params list spline parameters analysis. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Level Cluster β process_level_cluster","text":"list containing clustering results, including curve values design matrix.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Plots β process_plots","title":"Process Plots β process_plots","text":"Converts plots base64 appends HTML content.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Plots β process_plots","text":"","code":"process_plots( plots_element, plots_size, html_content, toc, header_index, element_name = NA )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Plots β process_plots","text":"plots_element list plots processed. plots_size list sizes plots. html_content current state HTML content. toc current state table contents (TOC). header_index index uniquely identify section anchoring. element_name character string specifying name element.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Plots β process_plots","text":"Updated HTML content plots included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":null,"dir":"Reference","previous_headings":"","what":"Process GSEA Result for a Specific Level β process_result","title":"Process GSEA Result for a Specific Level β process_result","text":"function processes GSEA result specific level. handles cases result contains `NA` values adding section break. Otherwise, extracts plot, plot size, header information result.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process GSEA Result for a Specific Level β process_result","text":"","code":"process_result(level_result, level_name)"},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process GSEA Result for a Specific Level β process_result","text":"level_result list containing GSEA result specific level. level_name character string representing name level.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process GSEA Result for a Specific Level β process_result","text":"list following components: plot plot object \"section_break\" result contains `NA`. plot_size integer indicating size plot. header_info list header information, including level name, full enrichment results, raw enrichment results available.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Top Table β process_top_table","title":"Process Top Table β process_top_table","text":"Processes top table LIMMA analysis, adding feature names intercepts.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Top Table β process_top_table","text":"","code":"process_top_table(process_within_level_result, feature_names)"},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Top Table β process_top_table","text":"process_within_level_result List lists containing limma topTable, fit. one specific level. feature_names non-empty character vector feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Top Table β process_top_table","text":"dataframe containing processed top table added intercepts.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Within Level β process_within_level","title":"Process Within Level β process_within_level","text":"Performs within-level analysis using limma generate top tables fit objects based specified spline parameters. Performs limma spline analysis selected level factor","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Within Level β process_within_level","text":"","code":"process_within_level( data, rna_seq_data, meta, design, dream_params, spline_params, level_index, padjust_method )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Within Level β process_within_level","text":"data matrix data values. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta dataframe containing metadata, including 'Time' column. design design formula matrix limma analysis. dream_params named list NULL. NULL, must least contain named element 'random_effects', must contain string formula random effects mixed models dream. Additionally, can contain named elements dof, must int bigger 1, degree freedom dream topTable, named element KenwardRoger, must bool, specifying whether use method . spline_params list spline parameters analysis. level_index index level within factor. padjust_method character string specifying p-adjustment method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Within Level β process_within_level","text":"list containing top table fit object limma analysis.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":null,"dir":"Reference","previous_headings":"","what":"Read and split section texts from a file β read_section_texts","title":"Read and split section texts from a file β read_section_texts","text":"internal function reads contents text file located `inst/descriptions` directory package splits individual sections based specified delimiter.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read and split section texts from a file β read_section_texts","text":"","code":"read_section_texts(filename)"},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read and split section texts from a file β read_section_texts","text":"filename character string specifying name file containing section texts. file located `inst/descriptions` directory package.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read and split section texts from a file β read_section_texts","text":"character vector element section text split delimiter `|`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Batch Effect β remove_batch_effect","title":"Remove Batch Effect β remove_batch_effect","text":"Removes batch effects data matrices using specified batch column metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Batch Effect β remove_batch_effect","text":"","code":"remove_batch_effect( datas, metas, meta_batch_column, meta_batch2_column, condition )"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Batch Effect β remove_batch_effect","text":"datas list matrices. metas list metadata corresponding data matrices. meta_batch_column character string specifying meta batch column. meta_batch2_column character string specifying second meta batch column. condition character vector length 1, specifying column name meta dataframe, contains levels separate experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Batch Effect β remove_batch_effect","text":"list matrices batch effects removed applicable.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"function removes batch effects data level specified condition. supports isolated integrated modes, optional handling second batch column.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"","code":"remove_batch_effect_cluster_hits( data, meta, condition, meta_batch_column, meta_batch2_column, design, mode, spline_params )"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"data dataframe containing main data. meta dataframe containing meta information. condition string specifying column `meta` divides experiment levels. meta_batch_column string specifying column `meta` indicates batch information. meta_batch2_column string specifying second batch column `meta`, applicable. design design matrix experiment. mode string indicating mode operation: \"isolated\" \"integrated\". spline_params list spline parameters design matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"list dataframes batch effects removed level.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"function operates two modes: isolated Processes level independently, using data level. integrated Processes entire dataset together. `meta_batch_column` specified, function removes batch effects using `removeBatchEffect`. second batch column (`meta_batch2_column`) specified, also included batch effect removal.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove intercept from a formula β remove_intercept","title":"Remove intercept from a formula β remove_intercept","text":"function modifies given formula replacing first occurrence standalone intercept (`1`) `0`. works even `1` preceded tilde (`~`), ensuring intercept removed leaving parts formula intact.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove intercept from a formula β remove_intercept","text":"","code":"remove_intercept(formula)"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove intercept from a formula β remove_intercept","text":"formula formula object. formula can include intercept (`1`) terms. `1` found, replaced `0`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove intercept from a formula β remove_intercept","text":"modified formula intercept removed. first standalone occurrence `1` replaced `0`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Prefix from String β remove_prefix","title":"Remove Prefix from String β remove_prefix","text":"Removes specified prefix beginning string. function useful cleaning standardizing strings removing known prefixes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Prefix from String β remove_prefix","text":"","code":"remove_prefix(string, prefix)"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Prefix from String β remove_prefix","text":"string string prefix removed. prefix string representing prefix removed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Prefix from String β remove_prefix","text":"string prefix removed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"function generates Gene Set Enrichment Analysis (GSEA) report based clustered hit levels, gene data, specified databases. processes input data, manages GSEA levels, produces HTML report plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"","code":"run_gsea( levels_clustered_hits, databases, report_info, clusterProfiler_params = NA, plot_titles = NA, universe = NULL, report_dir = here::here() )"},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"levels_clustered_hits list clustered hits different levels. databases list databases gene set enrichment analysis. report_info list containing information report generation. clusterProfiler_params Additional parameters GSEA analysis, default NA. include adj_p_value, pAdjustMethod, etc (see clusterProfiler documentation). plot_titles Titles plots, default NA. universe Enrichment background data, default NULL. report_dir Directory report saved, default `::()`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"list plots generated GSEA report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Central function of the package that runs a limma analysis with splines β run_limma_splines","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"function performs limma spline analysis identify significant time-dependent changes features (e.g., proteins) within omics time-series dataset. evaluates features within condition level levels comparing average differences interactions time condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"","code":"run_limma_splines(splineomics)"},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"splineomics S3 object class `SplineOmics` contains following elements: data: matrix omics dataset, feature names optionally row headers. rna_seq_data: object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta: dataframe containing metadata corresponding data, must include 'Time' column column specified condition. design: character string representing limma design formula. condition: character string specifying column name meta used define groups analysis. spline_params: list spline parameters used analysis, including: spline_type: type spline (e.g., \"n\" natural splines \"b\" B-splines). dof: Degrees freedom spline. knots: Positions internal knots (B-splines). bknots: Boundary knots (B-splines). degree: Degree spline (B-splines ).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"SplineOmics object, updated list three elements: - `time_effect`: list top tables level time effect. - `avrg_diff_conditions`: list top tables comparison levels. comparison average difference values. - `interaction_condition_time`: list top tables comparison levels. comparison interaction condition time.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":null,"dir":"Reference","previous_headings":"","what":"Allows to test different ","title":"Allows to test different ","text":"function screens various combinations hyperparameters limma analysis, including designs, modes, degrees freedom. validates inputs, generates results combinations, plots outcomes. Finally, may also involved generating HTML report part larger analysis workflow.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows to test different ","text":"","code":"screen_limma_hyperparams( splineomics, datas, datas_descr, metas, designs, modes, spline_test_configs, report_dir = here::here(), adj_pthresholds = c(0.05), rna_seq_datas = NULL, time_unit = \"min\", padjust_method = \"BH\" )"},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows to test different ","text":"splineomics S3 object class `SplineOmics` contains necessary data parameters analysis, including: condition: string specifying column name meta dataframe, contains levels separate experiment ('treatment' can condition, 'drug' 'drug' can levels condition). report_info: meta_batch_column: character string specifying meta batch column. meta_batch2_column: character string specifying second meta batch column (limma function removeBatchEffect supports maximum two batch columns.) datas list matrices containing datasets analyzed. datas_descr description object data. metas list data frames containing metadata dataset `datas`. designs character vector design formulas limma analysis. modes character vector must length 'designs'. design formula, must specify either 'isolated' 'integrated'. Isolated means limma determines results level using data level. Integrated means limma determines results levels using full dataset (levels). spline_test_configs configuration object spline tests. report_dir non-empty string specifying report directory. adj_pthresholds numeric vector p-value thresholds significance determination. rna_seq_datas list RNA-seq data objects, voom object derived limma::voom function. time_unit character string specifying time unit label plots. padjust_method character string specifying method p-value adjustment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Allows to test different ","text":"Returns list plots generated limma analysis results. element list corresponds different combination hyperparameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Default Parameters β set_default_params","title":"Set Default Parameters β set_default_params","text":"function checks provided `params` list `NA` missing elements. `params` `NA`, assigns list default parameters. element missing `params`, adds missing element respective default value.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Default Parameters β set_default_params","text":"","code":"set_default_params(params)"},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Default Parameters β set_default_params","text":"params list parameters checked updated default values necessary.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Default Parameters β set_default_params","text":"list parameters required elements, either input `params` added default values missing elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Shorten Names β shorten_names","title":"Shorten Names β shorten_names","text":"Replaces occurrences unique values within name first three characters. function useful abbreviating long condition names dataset.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shorten Names β shorten_names","text":"","code":"shorten_names(name, unique_values)"},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shorten Names β shorten_names","text":"name string representing name shortened. unique_values vector unique values whose abbreviations replace occurrences name.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shorten Names β shorten_names","text":"string unique values replaced abbreviations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":null,"dir":"Reference","previous_headings":"","what":"Stop with custom message without call. β stop_call_false","title":"Stop with custom message without call. β stop_call_false","text":"helper function triggers error specified message suppresses function call error output. function behaves similarly base `stop()` function automatically concatenates multiple message strings provided.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stop with custom message without call. β stop_call_false","text":"","code":"stop_call_false(...)"},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stop with custom message without call. β stop_call_false","text":"... One character strings specifying error message. multiple strings provided, concatenated space .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stop with custom message without call. β stop_call_false","text":"function return value; stops execution throws error.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Store Hits β store_hits","title":"Store Hits β store_hits","text":"Stores feature indices significant hits based adjusted p-value threshold condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Store Hits β store_hits","text":"","code":"store_hits(condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Store Hits β store_hits","text":"condition list containing dataframes parameters condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Store Hits β store_hits","text":"list element vector feature indices meet significance threshold.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Truncate Row Names β truncate_row_names","title":"Truncate Row Names β truncate_row_names","text":"function truncates row names exceed specified maximum length. row name length exceeds maximum length, appends \" ...\" indicate truncation.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Truncate Row Names β truncate_row_names","text":"","code":"truncate_row_names(names, max_length = 40)"},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Truncate Row Names β truncate_row_names","text":"names character vector row names. max_length integer specifying maximum length row names. Default 40.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Truncate Row Names β truncate_row_names","text":"character vector truncated row names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":null,"dir":"Reference","previous_headings":"","what":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"Updates SplineOmics object modifying existing fields adding new ones.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"","code":"update_splineomics(splineomics, ...)"},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"splineomics SplineOmics object updated. ... Named arguments new values fields updated added.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"updated SplineOmics object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Within level analysis β within_level","title":"Within level analysis β within_level","text":"Processes single level within condition, performing limma analysis generating top table results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Within level analysis β within_level","text":"","code":"within_level( level, level_index, spline_params, data, rna_seq_data, meta, design, dream_params, condition, feature_names, padjust_method, mode )"},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Within level analysis β within_level","text":"level level within condition process. level_index index level within condition. spline_params list spline parameters analysis. data matrix data values. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta dataframe containing metadata data. design design formula matrix limma analysis. dream_params named list NULL. NULL, must least contain named element 'random_effects', must contain string formula random effects mixed models dream. Additionally, can contain named elements dof, must int bigger 1, degree freedom dream topTable, named element KenwardRoger, must bool, specifying whether use method . condition character string specifying condition. feature_names non-empty character vector feature names. padjust_method character string specifying p-adjustment method. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Within level analysis β within_level","text":"list containing name results top table results.","code":""},{"path":[]}]
+[{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":null,"dir":"","previous_headings":"","what":"Changelog","title":"Changelog","text":"notable changes project documented file. format based Keep Changelog, project adheres Semantic Versioning.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"template-for-changelog-entries","dir":"","previous_headings":"","what":"Template for Changelog Entries","title":"Changelog","text":"release section follow structure :","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"added","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Added","title":"Changelog","text":"New features functionality introduced release.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"changed","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Changed","title":"Changelog","text":"Updates modifications existing features.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"fixed","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Fixed","title":"Changelog","text":"Bugs resolved.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"deprecated","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Deprecated","title":"Changelog","text":"Features still functional slated removal future.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"removed","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Removed","title":"Changelog","text":"Features functionality removed.","code":""},{"path":"https://csbg.github.io/SplineOmics/CHANGELOG.html","id":"security","dir":"","previous_headings":"Template for Changelog Entries > [Version] - YYYY-MM-DD","what":"Security","title":"Changelog","text":"security-related updates patches. Examples: - Added: Introduced new plotting function plotTimeSeries(). - Fixed: Resolved issue causing crashes input data missing values. - Changed: Modified default parameters normalizeData().","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing othersβ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement thomas.rauter@plus.ac.. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://csbg.github.io/SplineOmics/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozillaβs code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"pulling-the-docker-container","dir":"Articles","previous_headings":"","what":"Pulling the Docker Container","title":"Docker-instructions","text":"pull Docker container, use following command. Make sure check newest version specific version need visiting Docker Hub repository. face βpermission deniedβ issues, check vignette","code":"docker pull thomasrauter/splineomics:0.1.0"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"running-the-docker-container","dir":"Articles","previous_headings":"","what":"Running the Docker Container","title":"Docker-instructions","text":"run Docker container, can use one following commands, depending operating system. running command, ensure directory containing two subfolders: input output. used transfer files local machine Docker container. Linux macOS (Bash): Windows (PowerShell): container running, open web browser navigate http://localhost:8888. Log using following credentials: Username: rstudio Password: one set -e PASSWORD=123 option (123 case) long container running, can work localhost page RStudio, also SplineOmics package installed. /home/rstudio/ R session working folder. Stop container: Start container :","code":"docker run -it -d \\ -v $(pwd)/input:/home/rstudio/input \\ -v $(pwd)/output:/home/rstudio/output \\ -p 8888:8787 \\ -e PASSWORD=123 \\ --name splineomics \\ thomasrauter/splineomics:0.1.0 docker run -it -d ` -v \"${PWD}\\input:/home/rstudio/input\" ` -v \"${PWD}\\output:/home/rstudio/output\" ` -p 8888:8787 ` -e PASSWORD=123 ` --name splineomics ` thomasrauter/splineomics:0.1.0 docker stop splineomics docker start splineomics"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"using-the-docker-desktop-gui-instead","dir":"Articles","previous_headings":"","what":"Using the Docker Desktop gui instead","title":"Docker-instructions","text":"Instead pasting commands Power- Bashshell, can also graphical user interface Docker Desktop.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"input-and-output-file-management","dir":"Articles","previous_headings":"","what":"Input and Output File Management","title":"Docker-instructions","text":"input output directories local machine mounted corresponding directories inside Docker container. allows seamless file transfer local machine container. Place input files (e.g., data, metadata, annotation files) input directory local machine. files automatically appear /home/rstudio/input inside container. files generated RStudio within container saved /home/rstudio/output. files automatically appear output directory local machine.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"inspect-docker-container-installations","dir":"Articles","previous_headings":"","what":"Inspect Docker container installations","title":"Docker-instructions","text":"see R packages system installations make Docker container, can run following command terminal RStudio localhost browser page. /home/rstudio/output dir mounted local filesystem, make installation log files available .","code":"cp -r /log home/rstudio/output"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"installing-additional-r-packages-in-the-container","dir":"Articles","previous_headings":"","what":"Installing additional R packages in the container","title":"Docker-instructions","text":"New R packages can installed normal way: However, note packages installed running container lost container deleted rebuilt.","code":"install.packages(\"package_name\")"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"permanent-additions","dir":"Articles","previous_headings":"","what":"Permanent additions","title":"Docker-instructions","text":"want permanently add R packages, R scripts, files SplineOmics Docker image, can use base image building new image. ensure changes saved new image, rather lost container deleted. example: Run container new image commands described .","code":"# Use the SplineOmics image as the base image FROM thomasrauter/splineomics:0.1.0 # Install the data.table package permanently RUN R -e \"install.packages('data.table')\" # Optionally, add custom R scripts to the image COPY your_script.R /home/rstudio/your_script.R # Set the working directory WORKDIR /home/rstudio # Expose RStudio Server port EXPOSE 8787 # Start RStudio server CMD [\"/init\"] # Build new image: # docker build -t your_new_image_name ."},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"creating-a-reproducible-docker-container-with-automated-analysis","dir":"Articles","previous_headings":"","what":"Creating a Reproducible Docker Container with Automated Analysis","title":"Docker-instructions","text":"final analysis script inside Docker container SplineOmics package, want scientists can easily reproduce results running just one line code, follow guide . instruct create new image based container, can save example Docker Hub. Others can download image, run container get exact results got.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"prepare-your-analysis-and-scripts","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"1. Prepare your analysis and scripts","title":"Docker-instructions","text":"Ensure analysis scripts necessary files saved dedicated directory inside container (e.g., /home/rstudio/analysis/). analysis script take input files directory like /home/rstudio/input/ (already inside container need mounted reproducing analysis) output results /home/rstudio/output/. /home/rstudio/output/ directory mounted local directory userβs machine, making results accessible outside container. Example directory structure:","code":"/home/rstudio/ βββ analysis/ βββ final_analysis.R # Main analysis script βββ helper_functions.R # Supporting scripts"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"create-an-entry-point-script","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"2. Create an Entry Point Script","title":"Docker-instructions","text":"Create bash script (run_analysis.sh) runs analysis automatically. Example run_analysis.sh: Save script /home/rstudio/.","code":"#!/bin/bash Rscript /home/rstudio/analysis/final_analysis.R tail -f /dev/null # Keep the container running after analysis"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"commit-the-container-as-a-new-image-with-an-entry-point","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"3. Commit the Container as a New Image with an Entry Point","title":"Docker-instructions","text":"scripts ready, commit running container new image set new entry point run bash script automatically:","code":"docker commit \\ --change='CMD [\"/bin/bash\", \"/home/rstudio/run_analysis.sh\"]' \\ \\ thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"push-the-new-image-to-docker-hub","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"4. Push the New Image to Docker Hub","title":"Docker-instructions","text":"Push new image Docker Hub others can easily pull reproduce analysis: Others can pull (download) container command:","code":"docker push thomasrauter/splineomics-analysis:v1 docker pull thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"running-the-container-to-reproduce-the-results","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"5. Running the container to reproduce the results","title":"Docker-instructions","text":"reproduce results, need create local directory results saved mount directory containerβs /home/rstudio/output/ directory. Use following command run container ensure results saved local output directory (see commands section Running Docker Container mount output dir current working dir).","code":"docker run -it \\ -v /path/to/local/output:/home/rstudio/output \\ thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/Docker-instructions.html","id":"optional-getting-insights-into-the-full-analysis","dir":"Articles","previous_headings":"Creating a Reproducible Docker Container with Automated Analysis","what":"(Optional) Getting insights into the full analysis","title":"Docker-instructions","text":"Start new container mount empty local directory /home/rstudio/ directory inside container. allows directly access analysis files local machine.","code":"docker run -it \\ -v /path/to/local/dir:/home/rstudio \\ thomasrauter/splineomics-analysis:v1"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"about-this-vignette","dir":"Articles","previous_headings":"","what":"About this vignette","title":"RNA-seq analysis","text":"tutorial intends showcase explain capabilities SplineOmics package walking real complete RNA-seq example, start finish. SplineOmics explained detail get-started vignette, proteomics example covered. vignette focused showing RNA-seq data can used, , less details overall package provided .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"data-overview","dir":"Articles","previous_headings":"About this vignette","what":"Data Overview","title":"RNA-seq analysis","text":"dataset originates time-series RNA-seq experiment designed study Chinese Hamster Ovary (CHO) cells. experiment involved cultivating cells eight bioreactors, four bioreactors subjected temperature shift 146 hours (experimental condition) remaining four bioreactors maintained without temperature shift (control condition). Samples collected 17 distinct time points throughout experiment, specifically: \"72h\", \"76h\", \"96h\", \"120h\", \"124h\", \"144h\", \"148h\", \"152h\", \"168h\", \"192h\", \"216h\", \"220h\", \"240h\", \"264h\", \"268h\", \"288h\", \"312h\" cultivation start. time point sampled eight bioreactors, resulting total 136 samples. two batch effects: Reactor Plate. Different bioreactors, cells cultivated, can show big variablity. Plate refers two different plates used RNA-seq analysis samples. fully random design used distribute samples two plates. vignette, just remove plate batch effect. data matrix comprises genes rows samples columns, providing gene expression measurements time points. sample initially sequenced three technical replicates across two NovaSeq X flow cells. technical replicates collapsed generate final dataset used analysis. goal experiment investigate effect temperature shift CHO cell cultivation gene expression dynamics time. Note: original dataset, yet published time vignetteβs creation. demonstration purposes, genes randomly shuffled, subset data included reduce dataset size.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"analysis-goals","dir":"Articles","previous_headings":"About this vignette","what":"Analysis Goals","title":"RNA-seq analysis","text":"main objectives analysis : Identify genes significant temporal changes: Among thousands genes measured, goal identify exhibit significant changes expression time. Cluster genes based temporal patterns: Genes showing significant temporal changes (hits) grouped clusters based time-dependent expression patterns. Perform gene set enrichment analysis: cluster, gene set enrichment analysis conducted identify whether specific biological pathways processes - downregulated response feeding processes influenced temperature shift. Assess impact temperature shifts temporal patterns: analysis determine whether temporal patterns gene expression affected temperature shift, .e., whether gene expression dynamics differ time temperature shift conditions compared controls.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"load-the-packages","dir":"Articles","previous_headings":"","what":"Load the packages","title":"RNA-seq analysis","text":"","code":"library(SplineOmics) #> Warning: replacing previous import 'limma::topTable' by #> 'variancePartition::topTable' when loading 'SplineOmics' library(readr) # For reading the meta CSV file library(here) # For managing filepaths #> here() starts at /tmp/RtmpCTvFNy library(dplyr) # For data manipulation #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(knitr) # For Showing the head of the data and the meta tables."},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"load-the-files","dir":"Articles","previous_headings":"","what":"Load the files","title":"RNA-seq analysis","text":"","code":"data <- readRDS(xzfile(system.file( \"extdata\", \"rna_seq_data.rds.xz\", package = \"SplineOmics\" ))) meta <- readr::read_csv( system.file( \"extdata\", \"rna_seq_meta.csv\", package = \"SplineOmics\" ), show_col_types = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"show-top-rows-of-data","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of data","title":"RNA-seq analysis","text":"data: numeric matrix row represents gene (features) column corresponds sample. row names matrix contain gene identifiers, columns aligned sample metadata meta. matrix contains expression values 136 samples. Note study conducted blinded manner, samples randomly distributed across two plates RNA-seq analysis. result, sample numbers (e.g., 1, 2, 3, etc.) sequential order respect time, condition, plate. data analysis involving splines time, essential sort samples based time establish valid temporal sequence. Additionally, organizing data way improves clarity ensures consistency. Within time point, samples sorted condition (e.g., constant temp_shift) , subsequently, plate (e.g., plate_1 plate_2).","code":"kable( head(data), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"show-top-rows-of-meta","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of meta","title":"RNA-seq analysis","text":"meta: data frame containing metadata information samples data. row meta corresponds column data, ensuring 1:1 alignment metadata entries expression data samples. columns meta describe various attributes samples, SampleNr, Reactor, Time, Condition, Plate.","code":"kable( head(meta), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"preprocess-the-data","dir":"Articles","previous_headings":"","what":"Preprocess the data","title":"RNA-seq analysis","text":"Filter data rows (genes) zero counts across samples. step standard preprocessing procedure RNA-seq data analysis, genes zero counts samples provide information downstream analyses.","code":"rows_before <- nrow(data) # Filter data rows data <- data[rowSums(data) > 0, ] rows_after <- nrow(data) rows_removed <- rows_before - rows_after cat(sprintf( \"Rows before filtering: %d\\nRows after filtering: %d\\nRows removed: %d\\n\", rows_before, rows_after, rows_removed )) #> Rows before filtering: 1000 #> Rows after filtering: 944 #> Rows removed: 56"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"perform-eda-exploratory-data-analysis","dir":"Articles","previous_headings":"","what":"Perform EDA (exploratory data analysis)","title":"RNA-seq analysis","text":"can see HTML report explore_data() function batch-corrected data, report batch-corrected data.","code":"report_info <- list( omics_data_type = \"RNA\", data_description = \"RNA-seq data of CHO cells\", data_collection_date = \"December 2024\", analyst_name = \"Thomas Rauter\", contact_info = \"thomas.rauter@plus.ac.at\", project_name = \"DGTX\" ) report_dir <- here::here( \"results\", \"explore_data\" ) splineomics <- SplineOmics::create_splineomics( data = data, meta = meta, report_info = report_info, condition = \"Condition\", # Column of meta that contains the levels. meta_batch_column = \"Plate\" # Remove batch effect for plotting. ) plots <- SplineOmics::explore_data( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"run-limma-spline-analysis","dir":"Articles","previous_headings":"","what":"Run limma spline analysis","title":"RNA-seq analysis","text":"example, skipping finding best hyperparameters screen_limma_hyperparams() function, already clear idea use. can customize normalization method providing specific normalization function normalize_func argument preprocess_rna_seq_data() function. details use feature, please refer function documentation available βReferencesβ website. Additionally, use preprocess_rna_seq_data() optional RNA-seq data. Alternatively, can use limma::voom function directly pass resulting voom object rna_seq_data argument create_splineomics() update_splineomics(). Alongside , must pass $E data matrix data argument. general, long data argument contains actual data matrix rna_seq_data argument contains object compatible limma, data correctly processed. Run run_limma_splines() function updated SplineOmics object: output function run_limma_splines() named list, element specific βcategoryβ results. Refer document explanation different result categories. elements list, containing elements respective limma topTables, either level comparison two levels. element βtime_effectβ list, element topTable p-value feature respective level reported. element βavrg_diff_conditionsβ list contains elements topTables, represent comparison average differences levels. element βinteraction_condition_timeβ list contains elements topTables, represent interaction levels (includes time average differences)","code":"spline_params = list( spline_type = c(\"n\"), # natural cubic splines dof = c(3L) # Degree of freedom of 2 for the splines. ) voom_obj <- preprocess_rna_seq_data( raw_counts = data, meta = meta, spline_params = spline_params, design = \"~ 1 + Condition*X + Plate\" ) #> Preprocessing RNA-seq data (normalization + voom)... data <- voom_obj$E splineomics <- SplineOmics::update_splineomics( splineomics = splineomics, data = data, rna_seq_data = voom_obj, design = \"~ 1 + Condition*X + Plate\", mode = \"integrated\", # means limma uses the full data for each condition. spline_params = spline_params ) splineomics <- SplineOmics::run_limma_splines( splineomics = splineomics ) #> Hint: The data contains negative values. This may occur if the data has been transformed (e.g., log-transformed or normalized) and is valid in such cases. Ensure that the data preprocessing aligns with your analysis requirements. #> Column 'Plate' of meta will be used to remove the batch effect for the plotting #> Make sure that the design formula contains no interaction between the condition and time (X) for mode == isolated, and that it contains an interaction for mode == integrated. Otherwise, you will get an uncaught error of 'coefficients not estimable' or 'subscript out of bounds'. #> Info limma spline analysis completed successfully"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"build-limma-report","dir":"Articles","previous_headings":"","what":"Build limma report","title":"RNA-seq analysis","text":"topTables three limma result categories can used generate p-value histograms volcano plots. can view generated analysis report create_limma_report function .","code":"report_dir <- here::here( \"results\", \"limma_reports\" ) plots <- SplineOmics::create_limma_report( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"cluster-the-hits-significant-features","dir":"Articles","previous_headings":"","what":"Cluster the hits (significant features)","title":"RNA-seq analysis","text":"obtained limma spline results, can cluster hits based temporal pattern (spline shape). define hit setting adj. p-value threshold every level. Hits features (genes ) adj. p-value threshold. Hierarchical clustering used place every hit one many clusters specified specific level. Note dataset, vast amount hits. useful thousands individual plots, also takes long time compute resulting HTML large size, want limit hits plotted. several options: Use low adjusted p-value: approach filters significant features (genes) proceeding analysis visualization. Access customize data: Modify dataframes inside SplineOmics object removing selected set features (genes) plotting. Optimize clustering without generating report: Set report argument cluster_hits() function FALSE (default TRUE). skips generation HTML report, significantly speeding computation omitting creation export plots. can view generated analysis report cluster_hits function . discussed , three limma result categories. cluster_hits() report shows results three, present (category 2 3 can generated design formula contains interaction effect).","code":"# Note: The low adj. p-values are to have less results, so that the HTML report # is smaller in file size. adj_pthresholds <- c( # 0.05 for both levels 0.0000001, # constant (temperature) 0.00001 0.0000001 # temp_shift ) clusters <- c( 4L, # 4 clusters for constant 4L # 4 clusters for temp_shift ) report_dir <- here::here( \"results\", \"clustering_reports\" ) # Note: The treatment_labels and treatment_timepoints variable can be defined # much simpler for this experiment, because all plots should have the same # treatment line (it can be done like shown by the commented out arguments of # plot_info below). The reason why this is done in the explicit way here is to # show how it is possible to potentially have different treatment lines for the # \"double spline\" plots (the ones that compare limma result category 2 and 3). # constant_temp_shift is the first condition of the double spline plot and the # second condition separated by an underscore (it must be exactly like this, # otherwise it does not work. The first condition is always the one that occurs # first from the top in the respective meta column). treatment_labels = list( constant = \"temp shift\", temp_shift = \"temp shift\", constant_temp_shift = \"temp shift\" ) treatment_timepoints = list( constant = 146, temp_shift = 146, constant_temp_shift = 146 ) plot_info <- list( # For the spline plots y_axis_label = \"log2 intensity\", time_unit = \"min\", # our measurements were in minutes # treatment_labels = list(\"temp shift\"), # add this for all conditions # treatment_timepoints = list(146) # temp shift was at 146 hours. treatment_labels = treatment_labels, treatment_timepoints = treatment_timepoints ) genes <- rownames(data) plot_options <- list( # When meta_replicate_column is not there, all datapoints are blue. meta_replicate_column = \"Reactor\" # Colors the data points based on Reactor ) clustering_results <- SplineOmics::cluster_hits( splineomics = splineomics, adj_pthresholds = adj_pthresholds, clusters = clusters, genes = genes, plot_info = plot_info, plot_options = plot_options, report_dir = report_dir, adj_pthresh_avrg_diff_conditions = 0.0000001, adj_pthresh_interaction_condition_time = 0.0000001 )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"perform-gene-set-enrichment-analysis-gsea","dir":"Articles","previous_headings":"","what":"Perform gene set enrichment analysis (GSEA)","title":"RNA-seq analysis","text":"clustered hits identified, subsequent step gain biological insights perform GSEA. , respective genes can assigned clustered hit, GSEA can carried . proceed, Enrichr databases choice need downloaded: Per default file placed current working directory, root dir R project. run GSEA, downloaded database file loaded dataframe. , optionally, clusterProfiler parameters report dir can specified. function create_gsea_report() runs GSEA using clusterProfiler, generates HTML report returns GSEA dotplots R. function runs clusterProfiler clusters levels, generates HTML report: can view generated analysis report run_gsea function .","code":"# Specify which databases you want to download from Enrichr gene_set_lib <- c( \"WikiPathways_2019_Human\", \"NCI-Nature_2016\", \"TRRUST_Transcription_Factors_2019\", \"MSigDB_Hallmark_2020\", \"GO_Cellular_Component_2018\", \"CORUM\", \"KEGG_2019_Human\", \"TRANSFAC_and_JASPAR_PWMs\", \"ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\", \"GO_Biological_Process_2018\", \"GO_Molecular_Function_2018\", \"Human_Gene_Atlas\" ) SplineOmics::download_enrichr_databases( gene_set_lib = gene_set_lib, output_dir = here::here(), # output into the current working dir (default) filename = \"databases.tsv\" # just the name of the file, not the full path ) # Specify the filepath of the TSV file with the database info downloaded_dbs_filepath <- here::here( \"databases.tsv\" ) # Load the file databases <- read.delim( downloaded_dbs_filepath, sep = \"\\t\", stringsAsFactors = FALSE ) # Specify the clusterProfiler parameters clusterProfiler_params <- list( pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2 ) report_dir <- here::here( \"results\", \"gsea_reports\" ) result <- SplineOmics::run_gsea( # A dataframe with three columns: feature, cluster, and gene. Feature contains # the integer index of the feature, cluster the integer specifying the cluster # number, and gene the string of the gene, such as \"CLSTN2\". levels_clustered_hits = clustering_results$clustered_hits_levels, databases = databases, clusterProfiler_params = clusterProfiler_params, report_info = report_info, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA-seq analysis.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"RNA-seq analysis","text":"","code":"#> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Europe/Vienna #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices datasets utils methods base #> #> other attached packages: #> [1] knitr_1.48 dplyr_1.1.4 here_1.0.1 readr_2.1.5 #> [5] SplineOmics_0.1.0 #> #> loaded via a namespace (and not attached): #> [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.9 #> [4] shape_1.4.6.1 magrittr_2.0.3 farver_2.1.2 #> [7] nloptr_2.1.1 rmarkdown_2.28 GlobalOptions_0.1.2 #> [10] fs_1.6.4 ragg_1.3.3 vctrs_0.6.5 #> [13] minqa_1.2.8 base64enc_0.1-3 htmltools_0.5.8.1 #> [16] progress_1.2.3 broom_1.0.7 variancePartition_1.32.5 #> [19] sass_0.4.9 KernSmooth_2.23-22 bslib_0.8.0 #> [22] htmlwidgets_1.6.4 desc_1.4.3 pbkrtest_0.5.3 #> [25] plyr_1.8.9 cachem_1.1.0 lifecycle_1.0.4 #> [28] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.6-5 #> [31] R6_2.5.1 fastmap_1.2.0 rbibutils_2.3 #> [34] clue_0.3-65 digest_0.6.37 numDeriv_2016.8-1.1 #> [37] colorspace_2.1-1 patchwork_1.3.0 S4Vectors_0.40.2 #> [40] rprojroot_2.0.4 textshaping_0.4.0 fansi_1.0.6 #> [43] compiler_4.3.3 withr_3.0.1 bit64_4.5.2 #> [46] aod_1.3.3 doParallel_1.0.17 backports_1.5.0 #> [49] BiocParallel_1.36.0 viridis_0.6.5 dendextend_1.18.0 #> [52] gplots_3.2.0 MASS_7.3-60.0.1 rjson_0.2.23 #> [55] corpcor_1.6.10 gtools_3.9.5 caTools_1.18.3 #> [58] tools_4.3.3 zip_2.3.1 remaCor_0.0.18 #> [61] glue_1.8.0 nlme_3.1-164 grid_4.3.3 #> [64] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3 #> [67] gtable_0.3.5 tzdb_0.4.0 tidyr_1.3.1 #> [70] hms_1.1.3 utf8_1.2.4 BiocGenerics_0.48.1 #> [73] ggrepel_0.9.6 foreach_1.5.2 pillar_1.9.0 #> [76] stringr_1.5.1 vroom_1.6.5 limma_3.58.1 #> [79] circlize_0.4.16 splines_4.3.3 lattice_0.22-5 #> [82] renv_1.0.10 bit_4.5.0 tidyselect_1.2.1 #> [85] locfit_1.5-9.10 ComplexHeatmap_2.18.0 gridExtra_2.3 #> [88] IRanges_2.36.0 edgeR_4.0.16 svglite_2.1.3 #> [91] RhpcBLASctl_0.23-42 stats4_4.3.3 xfun_0.48 #> [94] Biobase_2.62.0 statmod_1.5.0 matrixStats_1.4.1 #> [97] pheatmap_1.0.12 stringi_1.8.4 yaml_2.3.10 #> [100] boot_1.3-29 evaluate_1.0.0 codetools_0.2-19 #> [103] tibble_3.2.1 BiocManager_1.30.25 cli_3.6.3 #> [106] systemfonts_1.1.0 Rdpack_2.6.1 munsell_0.5.1 #> [109] jquerylib_0.1.4 Rcpp_1.0.13 EnvStats_3.0.0 #> [112] png_0.1-8 parallel_4.3.3 pkgdown_2.1.1 #> [115] ggplot2_3.5.1 prettyunits_1.2.0 bitops_1.0-9 #> [118] lme4_1.1-35.5 viridisLite_0.4.2 mvtnorm_1.3-1 #> [121] lmerTest_3.1-3 scales_1.3.0 openxlsx_4.2.7.1 #> [124] purrr_1.0.2 crayon_1.5.3 fANCOVA_0.6-1 #> [127] GetoptLong_1.0.5 rlang_1.1.4"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"about-this-vignette","dir":"Articles","previous_headings":"","what":"About this vignette","title":"RNA_seq_analysis","text":"tutorial intends showcase explain capabilities SplineOmics package walking real complete RNA-seq example, start finish. SplineOmics explained detail get-started vignette, proteomics example covered. vignette focused showing RNA-seq data can used, , less details overall package provided .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"data-overview","dir":"Articles","previous_headings":"About this vignette","what":"Data Overview","title":"RNA_seq_analysis","text":"dataset originates time-series RNA-seq experiment designed study Chinese Hamster Ovary (CHO) cells. experiment involved cultivating cells eight bioreactors, four bioreactors subjected temperature shift 146 hours (experimental condition) remaining four bioreactors maintained without temperature shift (control condition). Samples collected 17 distinct time points throughout experiment, specifically: \"72h\", \"76h\", \"96h\", \"120h\", \"124h\", \"144h\", \"148h\", \"152h\", \"168h\", \"192h\", \"216h\", \"220h\", \"240h\", \"264h\", \"268h\", \"288h\", \"312h\" cultivation start. time point sampled eight bioreactors, resulting total 136 samples. two batch effects: Reactor Plate. Different bioreactors, cells cultivated, can show big variablity. Plate refers two different plates used RNA-seq analysis samples. fully random design used distribute samples two plates. vignette, just remove plate batch effect. data matrix comprises genes rows samples columns, providing gene expression measurements time points. sample initially sequenced three technical replicates across two NovaSeq X flow cells. technical replicates collapsed generate final dataset used analysis. goal experiment investigate effect temperature shift CHO cell cultivation gene expression dynamics time. Note: original dataset, yet published time vignetteβs creation. demonstration purposes, genes randomly shuffled, subset data included reduce dataset size.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"analysis-goals","dir":"Articles","previous_headings":"About this vignette","what":"Analysis Goals","title":"RNA_seq_analysis","text":"main objectives analysis : Identify genes significant temporal changes: Among thousands genes measured, goal identify exhibit significant changes expression time. Cluster genes based temporal patterns: Genes showing significant temporal changes (hits) grouped clusters based time-dependent expression patterns. Perform gene set enrichment analysis: cluster, gene set enrichment analysis conducted identify whether specific biological pathways processes - downregulated response feeding processes influenced temperature shift. Assess impact temperature shifts temporal patterns: analysis determine whether temporal patterns gene expression affected temperature shift, .e., whether gene expression dynamics differ time temperature shift conditions compared controls.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"load-the-packages","dir":"Articles","previous_headings":"","what":"Load the packages","title":"RNA_seq_analysis","text":"","code":"library(SplineOmics) #> Warning: replacing previous import 'limma::topTable' by #> 'variancePartition::topTable' when loading 'SplineOmics' library(readr) # For reading the meta CSV file library(here) # For managing filepaths #> here() starts at /home/thomas/Documents/PhD/projects/DGTX/R_packages/SplineOmics library(dplyr) # For data manipulation #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(knitr) # For Showing the head of the data and the meta tables."},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"load-the-files","dir":"Articles","previous_headings":"","what":"Load the files","title":"RNA_seq_analysis","text":"","code":"data <- readRDS(xzfile(system.file( \"extdata\", \"rna_seq_data.rds.xz\", package = \"SplineOmics\" ))) meta <- readr::read_csv( system.file( \"extdata\", \"rna_seq_meta.csv\", package = \"SplineOmics\" ), show_col_types = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"show-top-rows-of-data","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of data","title":"RNA_seq_analysis","text":"data: numeric matrix row represents gene (features) column corresponds sample. row names matrix contain gene identifiers, columns aligned sample metadata meta. matrix contains expression values 136 samples.","code":"kable( head(data), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"show-top-rows-of-meta","dir":"Articles","previous_headings":"Load the files","what":"Show top rows of meta","title":"RNA_seq_analysis","text":"meta: data frame containing metadata information samples data. row meta corresponds column data, ensuring 1:1 alignment metadata entries expression data samples. columns meta describe various attributes samples, SampleNr, Reactor, Time, Condition, Plate.","code":"kable( head(meta), format = \"markdown\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"preprocess-the-data","dir":"Articles","previous_headings":"","what":"Preprocess the data","title":"RNA_seq_analysis","text":"Filter data rows (genes) zero counts across samples. step standard preprocessing procedure RNA-seq data analysis, genes zero counts samples provide information downstream analyses.","code":"rows_before <- nrow(data) # Filter data rows data <- data[rowSums(data) > 0, ] rows_after <- nrow(data) rows_removed <- rows_before - rows_after cat(sprintf( \"Rows before filtering: %d\\nRows after filtering: %d\\nRows removed: %d\\n\", rows_before, rows_after, rows_removed )) #> Rows before filtering: 1000 #> Rows after filtering: 944 #> Rows removed: 56"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"perform-eda-exploratory-data-analysis","dir":"Articles","previous_headings":"","what":"Perform EDA (exploratory data analysis)","title":"RNA_seq_analysis","text":"can see HTML report explore_data() function batch-corrected data, report batch-corrected data.","code":"report_info <- list( omics_data_type = \"RNA\", data_description = \"RNA-seq data of CHO cells\", data_collection_date = \"December 2024\", analyst_name = \"Thomas Rauter\", contact_info = \"thomas.rauter@plus.ac.at\", project_name = \"DGTX\" ) report_dir <- here::here( \"results\", \"explore_data\" ) splineomics <- SplineOmics::create_splineomics( data = data, meta = meta, report_info = report_info, condition = \"Condition\", # Column of meta that contains the levels. meta_batch_column = \"Plate\" # Remove batch effect for plotting. ) plots <- SplineOmics::explore_data( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"run-limma-spline-analysis","dir":"Articles","previous_headings":"","what":"Run limma spline analysis","title":"RNA_seq_analysis","text":"example, skipping finding best hyperparameters screen_limma_hyperparams() function, already clear idea use. can customize normalization method providing specific normalization function normalize_func argument preprocess_rna_seq_data() function. details use feature, please refer function documentation available βReferencesβ website. Additionally, use preprocess_rna_seq_data() optional RNA-seq data. Alternatively, can use limma::voom function directly pass resulting voom object rna_seq_data argument create_splineomics() update_splineomics(). Alongside , must pass $E data matrix data argument. general, long data argument contains actual data matrix rna_seq_data argument contains object compatible limma, data correctly processed. Run run_limma_splines() function updated SplineOmics object: output function run_limma_splines() named list, element specific βcategoryβ results. Refer document explanation different result categories. elements list, containing elements respective limma topTables, either level comparison two levels. element βtime_effectβ list, element topTable p-value feature respective level reported. element βavrg_diff_conditionsβ list contains elements topTables, represent comparison average differences levels. element βinteraction_condition_timeβ list contains elements topTables, represent interaction levels (includes time average differences)","code":"spline_params = list( spline_type = c(\"n\"), # natural cubic splines dof = c(3L) # Degree of freedom of 2 for the splines. ) voom_obj <- preprocess_rna_seq_data( raw_counts = data, meta = meta, spline_params = spline_params, design = \"~ 1 + Condition*X + Plate\" ) #> Preprocessing RNA-seq data (normalization + voom)... data <- voom_obj$E splineomics <- SplineOmics::update_splineomics( splineomics = splineomics, data = data, rna_seq_data = voom_obj, design = \"~ 1 + Condition*X + Plate\", mode = \"integrated\", # means limma uses the full data for each condition. spline_params = spline_params ) splineomics <- SplineOmics::run_limma_splines( splineomics = splineomics ) #> Hint: The data contains negative values. This may occur if the data has been transformed (e.g., log-transformed or normalized) and is valid in such cases. Ensure that the data preprocessing aligns with your analysis requirements. #> Column 'Plate' of meta will be used to remove the batch effect for the plotting #> Make sure that the design formula contains no interaction between the condition and time (X) for mode == isolated, and that it contains an interaction for mode == integrated. Otherwise, you will get an uncaught error of 'coefficients not estimable' or 'subscript out of bounds'. #> Info limma spline analysis completed successfully"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"build-limma-report","dir":"Articles","previous_headings":"","what":"Build limma report","title":"RNA_seq_analysis","text":"topTables three limma result categories can used generate p-value histograms volcano plots. can view generated analysis report create_limma_report function .","code":"report_dir <- here::here( \"results\", \"limma_reports\" ) plots <- SplineOmics::create_limma_report( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"cluster-the-hits-significant-features","dir":"Articles","previous_headings":"","what":"Cluster the hits (significant features)","title":"RNA_seq_analysis","text":"obtained limma spline results, can cluster hits based temporal pattern (spline shape). define hit setting adj. p-value threshold every level. Hits features (genes ) adj. p-value threshold. Hierarchical clustering used place every hit one many clusters specified specific level. Note dataset, vast amount hits. useful thousands individual plots, also takes long time compute resulting HTML large size, want limit hits plotted. several options: Use low adjusted p-value: approach filters significant features (genes) proceeding analysis visualization. Access customize data: Modify dataframes inside SplineOmics object removing selected set features (genes) plotting. Optimize clustering without generating report: Set report argument cluster_hits() function FALSE (default TRUE). skips generation HTML report, significantly speeding computation omitting creation export plots. can view generated analysis report cluster_hits function . discussed , three limma result categories. cluster_hits() report shows results three, present (category 2 3 can generated design formula contains interaction effect).","code":"# Note: The low adj. p-values are to have less results, so that the HTML report # is smaller in file size. adj_pthresholds <- c( # 0.05 for both levels 0.0000001, # constant (temperature) 0.00001 0.0000001 # temp_shift ) clusters <- c( 4L, # 4 clusters for constant 4L # 4 clusters for temp_shift ) report_dir <- here::here( \"results\", \"clustering_reports\" ) # Note: The treatment_labels and treatment_timepoints variable can be defined # much simpler for this experiment, because all plots should have the same # treatment line (it can be done like shown by the commented out arguments of # plot_info below). The reason why this is done in the explicit way here is to # show how it is possible to potentially have different treatment lines for the # \"double spline\" plots (the ones that compare limma result category 2 and 3). # constant_temp_shift is the first condition of the double spline plot and the # second condition separated by an underscore (it must be exactly like this, # otherwise it does not work. The first condition is always the one that occurs # first from the top in the respective meta column). treatment_labels = list( constant = \"temp shift\", temp_shift = \"temp shift\", constant_temp_shift = \"temp shift\" ) treatment_timepoints = list( constant = 146, temp_shift = 146, constant_temp_shift = 146 ) plot_info <- list( # For the spline plots y_axis_label = \"log2 intensity\", time_unit = \"min\", # our measurements were in minutes # treatment_labels = list(\"temp shift\"), # add this for all conditions # treatment_timepoints = list(146) # temp shift was at 146 hours. treatment_labels = treatment_labels, treatment_timepoints = treatment_timepoints ) genes <- rownames(data) plot_options <- list( # When meta_replicate_column is not there, all datapoints are blue. meta_replicate_column = \"Reactor\" # Colors the data points based on Reactor ) clustering_results <- SplineOmics::cluster_hits( splineomics = splineomics, adj_pthresholds = adj_pthresholds, clusters = clusters, genes = genes, plot_info = plot_info, plot_options = plot_options, report_dir = report_dir, adj_pthresh_avrg_diff_conditions = 0.0000001, adj_pthresh_interaction_condition_time = 0.0000001 )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"perform-gene-set-enrichment-analysis-gsea","dir":"Articles","previous_headings":"","what":"Perform gene set enrichment analysis (GSEA)","title":"RNA_seq_analysis","text":"Usually, final step bioinformatics analysis GSEA. clustered hit, respective gene can assigned GSEA performed. , Enrichr databases choice downloaded: Per default file placed current working directory, root dir R project. run GSEA, downloaded database file loaded dataframe. , optionally, clusterProfiler parameters report dir can specified. function create_gsea_report() runs GSEA using clusterProfiler, generates HTML report returns GSEA dotplots R. function runs clusterProfiler clusters levels, generates HTML report: can view generated analysis report run_gsea function .","code":"# Specify which databases you want to download from Enrichr gene_set_lib <- c( \"WikiPathways_2019_Human\", \"NCI-Nature_2016\", \"TRRUST_Transcription_Factors_2019\", \"MSigDB_Hallmark_2020\", \"GO_Cellular_Component_2018\", \"CORUM\", \"KEGG_2019_Human\", \"TRANSFAC_and_JASPAR_PWMs\", \"ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\", \"GO_Biological_Process_2018\", \"GO_Molecular_Function_2018\", \"Human_Gene_Atlas\" ) SplineOmics::download_enrichr_databases( gene_set_lib = gene_set_lib, output_dir = here::here(), # output into the current working dir (default) filename = \"databases.tsv\" # just the name of the file, not the full path ) # Specify the filepath of the TSV file with the database info downloaded_dbs_filepath <- here::here( \"databases.tsv\" ) # Load the file databases <- read.delim( downloaded_dbs_filepath, sep = \"\\t\", stringsAsFactors = FALSE ) # Specify the clusterProfiler parameters clusterProfiler_params <- list( pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2 ) report_dir <- here::here( \"results\", \"gsea_reports\" ) result <- SplineOmics::run_gsea( # A dataframe with three columns: feature, cluster, and gene. Feature contains # the integer index of the feature, cluster the integer specifying the cluster # number, and gene the string of the gene, such as \"CLSTN2\". levels_clustered_hits = clustering_results$clustered_hits_levels, databases = databases, clusterProfiler_params = clusterProfiler_params, report_info = report_info, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/RNA_seq_analysis.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"RNA_seq_analysis","text":"","code":"#> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Europe/Vienna #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices datasets utils methods base #> #> other attached packages: #> [1] knitr_1.48 dplyr_1.1.4 here_1.0.1 readr_2.1.5 #> [5] SplineOmics_0.1.0 #> #> loaded via a namespace (and not attached): #> [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.9 #> [4] shape_1.4.6.1 magrittr_2.0.3 farver_2.1.2 #> [7] nloptr_2.1.1 rmarkdown_2.28 GlobalOptions_0.1.2 #> [10] fs_1.6.4 ragg_1.3.3 vctrs_0.6.5 #> [13] minqa_1.2.8 base64enc_0.1-3 htmltools_0.5.8.1 #> [16] progress_1.2.3 broom_1.0.7 variancePartition_1.32.5 #> [19] sass_0.4.9 KernSmooth_2.23-22 bslib_0.8.0 #> [22] htmlwidgets_1.6.4 desc_1.4.3 pbkrtest_0.5.3 #> [25] plyr_1.8.9 cachem_1.1.0 lifecycle_1.0.4 #> [28] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.6-5 #> [31] R6_2.5.1 fastmap_1.2.0 rbibutils_2.3 #> [34] clue_0.3-65 digest_0.6.37 numDeriv_2016.8-1.1 #> [37] colorspace_2.1-1 patchwork_1.3.0 S4Vectors_0.40.2 #> [40] rprojroot_2.0.4 textshaping_0.4.0 fansi_1.0.6 #> [43] compiler_4.3.3 withr_3.0.1 bit64_4.5.2 #> [46] aod_1.3.3 doParallel_1.0.17 backports_1.5.0 #> [49] BiocParallel_1.36.0 viridis_0.6.5 dendextend_1.18.0 #> [52] gplots_3.2.0 MASS_7.3-60.0.1 rjson_0.2.23 #> [55] corpcor_1.6.10 gtools_3.9.5 caTools_1.18.3 #> [58] tools_4.3.3 zip_2.3.1 remaCor_0.0.18 #> [61] glue_1.8.0 nlme_3.1-164 grid_4.3.3 #> [64] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3 #> [67] gtable_0.3.5 tzdb_0.4.0 tidyr_1.3.1 #> [70] hms_1.1.3 utf8_1.2.4 BiocGenerics_0.48.1 #> [73] ggrepel_0.9.6 foreach_1.5.2 pillar_1.9.0 #> [76] stringr_1.5.1 vroom_1.6.5 limma_3.58.1 #> [79] circlize_0.4.16 splines_4.3.3 lattice_0.22-5 #> [82] renv_1.0.10 bit_4.5.0 tidyselect_1.2.1 #> [85] locfit_1.5-9.10 ComplexHeatmap_2.18.0 gridExtra_2.3 #> [88] IRanges_2.36.0 edgeR_4.0.16 svglite_2.1.3 #> [91] RhpcBLASctl_0.23-42 stats4_4.3.3 xfun_0.48 #> [94] Biobase_2.62.0 statmod_1.5.0 matrixStats_1.4.1 #> [97] pheatmap_1.0.12 stringi_1.8.4 yaml_2.3.10 #> [100] boot_1.3-29 evaluate_1.0.0 codetools_0.2-19 #> [103] tibble_3.2.1 BiocManager_1.30.25 cli_3.6.3 #> [106] systemfonts_1.1.0 Rdpack_2.6.1 munsell_0.5.1 #> [109] jquerylib_0.1.4 Rcpp_1.0.13 EnvStats_3.0.0 #> [112] png_0.1-8 parallel_4.3.3 pkgdown_2.1.1 #> [115] ggplot2_3.5.1 prettyunits_1.2.0 bitops_1.0-9 #> [118] lme4_1.1-35.5 viridisLite_0.4.2 mvtnorm_1.3-1 #> [121] lmerTest_3.1-3 scales_1.3.0 openxlsx_4.2.7.1 #> [124] purrr_1.0.2 crayon_1.5.3 fANCOVA_0.6-1 #> [127] GetoptLong_1.0.5 rlang_1.1.4"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Designing a Limma Design Formula","text":"limma package powerful tool analyzing gene expression data, particularly context microarrays RNA-seq. critical part limma analysis design formula, specifies experimental conditions contrasts interested . vignette provides guide construct limma design formula correctly, examples best practices.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"understanding-the-design-matrix","dir":"Articles","previous_headings":"","what":"Understanding the Design Matrix","title":"Designing a Limma Design Formula","text":"design matrix crucial component differential expression analysis using limma. defines relationships samples experimental conditions (factors) investigation. well-constructed design matrix allows limma correctly model effects factors estimate differential expression.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"basic-design-formula","dir":"Articles","previous_headings":"Understanding the Design Matrix","what":"Basic Design Formula","title":"Designing a Limma Design Formula","text":"simplest form, design formula includes one factor, treatment vs.Β control. experiment involves comparing two conditions (e.g., treated vs.Β untreated), can create design formula like : , condition factor variable metadata (meta) represents experimental groups. Important Points: ~ 0 + condition syntax tells R create design matrix without intercept (.e., matrix level factor condition represented column). approach helpful want make direct comparisons conditions. Including Multiple Factors experiment includes one factor, time points treatments, can include design formula: formula assumes effects treatment time additive (interaction). suspect interaction treatment time might important, can include interaction term: Interaction Term: treatment * time term includes main effects treatment time interaction. Blocking Factors experiments, might technical biological replicates, blocking factors (e.g., batch effects). can include blocking factors design formula: formula accounts treatment batch effects, ensuring analysis confounded batch effects.","code":"design <- model.matrix(~ 0 + condition, data = meta) design <- model.matrix(~ 0 + treatment + time, data = meta) design <- model.matrix(~ 0 + treatment * time, data = meta) design <- model.matrix(~ 0 + treatment + batch, data = meta)"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"creating-contrasts","dir":"Articles","previous_headings":"","what":"Creating Contrasts","title":"Designing a Limma Design Formula","text":"defining design matrix, likely want make specific comparisons conditions. contrasts come . example, compare treated vs.Β untreated, can define contrast matrix:","code":"contrast <- makeContrasts( treated_vs_untreated = treatmenttreated - treatmentuntreated, levels = design )"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"practical-example","dir":"Articles","previous_headings":"","what":"Practical Example","title":"Designing a Limma Design Formula","text":"Letβs say experiment two treatments (B) two time points (early late). metadata might look like : design formula : contrast compare treatment early time point treatment B late time point :","code":"meta <- data.frame( sample = c(\"S1\", \"S2\", \"S3\", \"S4\"), treatment = factor(c(\"A\", \"A\", \"B\", \"B\")), time = factor(c(\"early\", \"late\", \"early\", \"late\")) ) design <- model.matrix(~ 0 + treatment * time, data = meta) contrast <- makeContrasts( A_early_vs_B_late = (treatmentA:timeearly) - (treatmentB:timelate), levels = design )"},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Designing a Limma Design Formula","text":"Starting ~ 0 means intercept (.e., including baseline group model). Starting ~ 1 (just ~) includes intercept (baseline group). Factors separated + indicate additive effects. example, ~ 0 + factor1 + factor2 means modeling effects factor1 factor2 additively, without considering interactions. * symbol used model interactions factors. example, ~ 0 + factor1 * factor2 include factor1, factor2, interaction (factor1:factor2). Alternatively, can specify interaction explicitly :. example, ~ 0 + factor1 + factor2 + factor1:factor2 equivalent ~ 0 + factor1 * factor2.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"some-examples","dir":"Articles","previous_headings":"Summary","what":"Some examples:","title":"Designing a Limma Design Formula","text":"~ 0 + factor1 + factor2: Additive model without intercept. ~ 1 + factor1 + factor2: Additive model intercept. ~ 0 + factor1 * factor2: Model main effects interaction, intercept. ~ 1 + factor1 * factor2: Model intercept, main effects, interaction.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/design_limma_design_formula.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"Designing a Limma Design Formula","text":"","code":"## R version 4.3.3 (2024-02-29) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 22.04.5 LTS ## ## Matrix products: default ## BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 ## ## locale: ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ## [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 ## [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 ## [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C ## ## time zone: Europe/Vienna ## tzcode source: system (glibc) ## ## attached base packages: ## [1] stats graphics grDevices datasets utils methods base ## ## loaded via a namespace (and not attached): ## [1] digest_0.6.37 desc_1.4.3 R6_2.5.1 ## [4] fastmap_1.2.0 xfun_0.48 cachem_1.1.0 ## [7] knitr_1.48 htmltools_0.5.8.1 rmarkdown_2.28 ## [10] lifecycle_1.0.4 cli_3.6.3 sass_0.4.9 ## [13] pkgdown_2.1.1 textshaping_0.4.0 jquerylib_0.1.4 ## [16] renv_1.0.10 systemfonts_1.1.0 compiler_4.3.3 ## [19] rstudioapi_0.16.0 tools_4.3.3 ragg_1.3.3 ## [22] bslib_0.8.0 evaluate_1.0.0 yaml_2.3.10 ## [25] BiocManager_1.30.25 jsonlite_1.8.9 htmlwidgets_1.6.4 ## [28] rlang_1.1.4 fs_1.6.4"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"about-this-tutorial","dir":"Articles","previous_headings":"","what":"About this tutorial","title":"get-started","text":"tutorial intends showcase explain capabilities SplineOmics package walking real complete example, start finish.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"example-overview","dir":"Articles","previous_headings":"About this tutorial","what":"Example Overview","title":"get-started","text":"example involves time-series proteomics experiment, CHO (chinese hamster ovary) cells cultivated three bioreactors (three biological replicates). experiment includes following setup: Samples taken exponential stationary growth phases. 60 minutes feeding 15, 60, 90, 120, 240 minutes feeding","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"analysis-goals","dir":"Articles","previous_headings":"About this tutorial","what":"Analysis Goals","title":"get-started","text":"main goals analysis : Identify proteins significant temporal changes: 7162 cellular proteins, objective detect proteins show significant change time CHO cells fed (.e., impact feeding). Cluster hits based temporal patterns: proteins (hits) significant temporal changes clustered according time-based patterns. Perform gene set enrichment analysis: cluster, gene set enrichment analysis performed determine specific biological processes - downregulated feeding.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"note","dir":"Articles","previous_headings":"About this tutorial","what":"Note","title":"get-started","text":"documentation SplineOmics package functions can viewed ","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"load-the-packages","dir":"Articles","previous_headings":"","what":"Load the packages","title":"get-started","text":"","code":"library(SplineOmics) #> Warning: replacing previous import 'limma::topTable' by #> 'variancePartition::topTable' when loading 'SplineOmics' library(readxl) # for loading Excel files library(here) # For managing filepaths #> here() starts at /home/thomas/Documents/PhD/projects/DGTX/R_packages/SplineOmics library(dplyr) # For data manipulation #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"load-the-files","dir":"Articles","previous_headings":"","what":"Load the files","title":"get-started","text":"example, proteomics_data.rds file contains numeric values (intensities) also feature descriptions, gene protein name (= annotation part). Usually, load data example Excel file, .rds file compressed, reason format chosen limit size SplineOmics package. file meta.xlsx contains meta information, descriptions columns numeric values data. (example files part package donβt present system). Please note dataset actual experimental dataset, annotation information, gene names, removed since yet published time making SplineOmics package public. Instead, dataset includes randomly generated gene symbols gene names corresponding Cricetulus griseus (Chinese Hamster) row. intended demonstrate functionality package. left part data contains numeric values, right part annotation info, can copied separate dataframe, shown . Three comments characteristics input data : data must contain NA values special values, must consist numbers. example, original proteomics data contained NA values, resolved case imputation (replacing NA values numbers). features data ideally normally distributed analyzed limma, fits linear model feature. models rely statistical tests assume normality. Although limma can still function data normally distributed, resulting p-values may become less reliable. reason, strongly recommended transform data using techniques log2 transformation features deviate normality. Proper transformation helps ensure assumptions underlying statistical tests met, leading accurate trustworthy results. samples data independent . Linear models, used limma, assume observations (samples) independent. dependency samples (e.g., repeated measurements subject), assumption violated, can lead incorrect statistical inferences.","code":"data <- readRDS(xzfile(system.file( \"extdata\", \"proteomics_data.rds.xz\", package = \"SplineOmics\" ))) meta <- read_excel( system.file( \"extdata\", \"proteomics_meta.xlsx\", package = \"SplineOmics\" ) ) # Extract the annotation part from the dataframe. first_na_col <- which(is.na(data[1, ]))[1] annotation <- data |> dplyr::select((first_na_col + 1):ncol(data)) |> dplyr::slice(-c(1:3)) print(head(data)) #> # A tibble: 6 Γ 40 #> `Sample ID` `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` #> #> 1 Reactor E09 E10 E12 E09 E10 E12 E09 E10 E12 E09 E10 #> 2 Time Point TP01 TP01 TP01 TP02 TP02 TP02 TP03 TP03 TP03 TP04 TP04 #> 3 Phase of Feβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ Expoβ¦ #> 4 NA 15.2β¦ 15.2β¦ 15.3β¦ 15.1β¦ 15.2β¦ 15.0β¦ 15.2β¦ 15.2β¦ 15.2β¦ 15.1β¦ 15.2β¦ #> 5 NA 15.0β¦ 15.1β¦ 15.2β¦ 15.1β¦ 15.1β¦ 15.2β¦ 15.2β¦ 15.3β¦ 15.2β¦ 15.1β¦ 15.1β¦ #> 6 NA 14.5β¦ 14.7β¦ 14.6β¦ 14.5β¦ 14.6β¦ 14.6β¦ 14.5β¦ 14.6β¦ 14.7β¦ 14.5β¦ 14.6β¦ #> # βΉ 28 more variables: `12` , `13` , `14` , `15` , #> # `16` , `17` , `18` , `19` , `20` , `21` , #> # `22` , `23` , `24` , `25` , `26` , `27` , #> # `28` , `29` , `30` , `31` , `32` , `33` , #> # `34` , `35` , `36` , ...38 , Gene_symbol , #> # Gene_name print(meta) #> # A tibble: 36 Γ 5 #> Sample.ID Reactor Time.Point Phase Time #> #> 1 E09_TP01_Exponential E09 TP01 Exponential -60 #> 2 E10_TP01_Exponential E10 TP01 Exponential -60 #> 3 E12_TP01_Exponential E12 TP01 Exponential -60 #> 4 E09_TP02_Exponential E09 TP02 Exponential 15 #> 5 E10_TP02_Exponential E10 TP02 Exponential 15 #> 6 E12_TP02_Exponential E12 TP02 Exponential 15 #> 7 E09_TP03_Exponential E09 TP03 Exponential 60 #> 8 E10_TP03_Exponential E10 TP03 Exponential 60 #> 9 E12_TP03_Exponential E12 TP03 Exponential 60 #> 10 E09_TP04_Exponential E09 TP04 Exponential 90 #> # βΉ 26 more rows print(annotation) #> # A tibble: 4,162 Γ 2 #> Gene_symbol Gene_name #> #> 1 LOC113838844 cone-rod homeobox protein-like #> 2 Wdr83os WD repeat domain 83 opposite strand #> 3 Cubn cubilin #> 4 Dynlt1 dynein light chain Tctex-type 1 #> 5 Ostc oligosaccharyltransferase complex non-catalytic subunit #> 6 Unc5cl unc-5 family C-terminal like #> 7 Cfl1 cofilin 1 #> 8 LOC100752202 HEN methyltransferase 1 #> 9 LOC100755162 acyl-coenzyme A synthetase ACSM5, mitochondrial #> 10 LOC100768921 40S ribosomal protein S21 #> # βΉ 4,152 more rows"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"bring-the-inputs-into-the-standardized-format","dir":"Articles","previous_headings":"Load the files","what":"Bring the Inputs into the Standardized Format","title":"get-started","text":"Since data format required SplineOmics package, needs processing. SplineOmics package requires data numeric matrix, element allowed anything else number. can done commands R, file specific structure, function extract_data() can handle automatically.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"file-structure-requirements","dir":"Articles","previous_headings":"Load the files > Bring the Inputs into the Standardized Format","what":"File Structure Requirements","title":"get-started","text":"file looks like one used , : data matrix field left annotation info right fields separated one empty column","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"usage-of-the-extract_data-function","dir":"Articles","previous_headings":"Load the files > Bring the Inputs into the Standardized Format","what":"Usage of the extract_data() function","title":"get-started","text":", extract_data() can: Identify data matrix field return numeric matrix. Create column headers information written cells respective columns data matrix field. annotation columns specified, rowheaders increasing numbers. annotation columns specified (like \"First.Protein.Description\" \"ID\" example), combined form rowheaders (feature names).","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"usage-in-plotting","dir":"Articles","previous_headings":"Load the files > Bring the Inputs into the Standardized Format","what":"Usage in Plotting","title":"get-started","text":"generated rowheaders used label plots feature shown individually, : Spline plots datapoints individual feature.","code":"data <- SplineOmics::extract_data( # The dataframe with the numbers on the left and info on the right. data = data, # Use this annotation column for the feature names. feature_name_columns = c(\"Gene_name\"), # When TRUE, you must confirm that data is in the required format. user_prompt = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"perform-eda-exploratory-data-analysis","dir":"Articles","previous_headings":"","what":"Perform EDA (exploratory data analysis)","title":"get-started","text":"Now data required format (numeric matrix) can go . first step analyzing data typically Exploratory Data Analysis (EDA). EDA involves summarizing main characteristics data, often visualizations.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"common-eda-plots","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Common EDA Plots","title":"get-started","text":"common types EDA plots include: Density distributions Boxplots PCA (Principal Component Analysis) Correlation heatmaps , can generate plots lines R code. However, prefer, convenience, explore_data() function can handle .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"using-explore_data-for-eda","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Using explore_data() for EDA","title":"get-started","text":"SplineOmics package provides function explore_data() perform EDA. function requires following arguments: data: numeric data matrix. meta: metadata table. condition: name column metadata contains levels experiment (e.g., βExponentialβ βStationaryβ). report_info: list contains general information analysis, name analyst datatype (e.g. proteomics)","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"optional-arguments","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Optional Arguments","title":"get-started","text":"addition required arguments, explore_data() offers several optional arguments: meta_batch_column: name column contains first batch effect. meta_batch2_column: name column contains second batch effect. least one batch column provided, function : Use removeBatchEffect() function limma remove batch effect data plotting. Generate two EDA HTML reports: one uncorrected data one batch-corrected data.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"output-and-report-options","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Output and Report Options","title":"get-started","text":"default, reports saved current working directory, location can changed using report_dir argument. function also returns plots generated analysis, can modify according needs. want report generated, can set report argument FALSE (example just want figures R environment)","code":"# Those fields are mandatory, because we believe that when such a report is # opened after half a year, those infos can be very helpful. report_info <- list( omics_data_type = \"PTX\", data_description = \"Proteomics data of CHO cells\", data_collection_date = \"February 2024\", analyst_name = \"Thomas Rauter\", contact_info = \"thomas.rauter@plus.ac.at\", project_name = \"DGTX\" ) report_dir <- here::here( \"results\", \"explore_data\" )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"splineomics-object","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"SplineOmics Object","title":"get-started","text":"SplineOmics package, multiple functions take arguments input. make easier avoid errors, decided arguments provided individually functions, stored R6 object (type βSplineOmicsβ) object passed functions. Additionally, functions generate intermediate output, just necessary next function workflow, also just passed along updating SplineOmics object. donβt worry .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"functionality","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > SplineOmics Object","what":"Functionality","title":"get-started","text":"SplineOmics object can seen container necessary arguments stored. function retrieves required arguments object potentially adds new data results back .","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"documentation","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > SplineOmics Object","what":"Documentation","title":"get-started","text":"documentation function creates SplineOmics object can found documentation function updates [documentation function takes SplineOmics object input specifies arguments must present SplineOmics object passed respective function.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"required-arguments-create_splineomics","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Required Arguments create_splineomics()","title":"get-started","text":"data: matrix data meta: Metadata associated data. condition: Meta column name levels (e.g., Exponential Stationary).","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"optional-arguments-create_splineomics","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Optional Arguments create_splineomics()","title":"get-started","text":"rna_seq_data: object containing preprocessed RNA-seq data, output limma::voom function. annotation: dataframe feature descriptions data. report_info: list containing general information analysis. meta_batch_column: Column meta batch information. meta_batch2_column: Column secondary meta batch information. design: limma design formula spline_params: Parameters spline functions. Now SplineOmics object defined, can perform exploratory data analysis. can see HTML report explore_data() function batch-corrected data, report batch-corrected data. EDA plots can tell range things. plots HTML report grouped three categories: Distribution Variability Analysis, Time Series Analysis, Dimensionality Reduction Clustering. look correlation heatmaps HTML report, can see samples E12_TP05_Exponential E10_TP10_Stationary stick . Seeing , might want remove data. can test happens , along testing hyperparameter choices influence results, package function screen_limma_hyperparams().","code":"# splineomics now contains the SplineOmics object. splineomics <- SplineOmics::create_splineomics( data = data, meta = meta, annotation = annotation, report_info = report_info, condition = \"Phase\", # Column of meta that contains the levels. meta_batch_column = \"Reactor\" # For batch effect removal ) # Special print.SplineOmics function leads to selective printing print(splineomics) #> data:SplineOmics Object #> ------------------- #> Number of features (rows): 4162 #> Number of samples (columns): 36 #> Meta data columns: 5 #> First few meta columns: #> # A tibble: 3 Γ 5 #> Sample.ID Reactor Time.Point Phase Time #> #> 1 E09_TP01_Exponential E09 TP01 Exponential -60 #> 2 E10_TP01_Exponential E10 TP01 Exponential -60 #> 3 E12_TP01_Exponential E12 TP01 Exponential -60 #> Condition: Phase #> No RNA-seq data provided. #> Annotation provided with 4162 entries. #> No spline parameters set. #> P-value adjustment method: BH plots <- SplineOmics::explore_data( splineomics = splineomics, # SplineOmics object report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"finding-the-best-hyperparameters","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis)","what":"Finding the Best Hyperparameters","title":"get-started","text":"running limma spline analysis, important find best βhyperparametersβ. context, hyperparameters include: Degree freedom (DoF) Different versions data (e.g., outlier removed vs.Β removed) Different limma design formulas","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"challenge-of-hyperparameter-selection","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Challenge of Hyperparameter Selection","title":"get-started","text":"Rationally determining best combination hyperparameters can challenging. rationally, mean deciding upon final hyperparameters without ever testing , just scientific reasoning. much easier just testing seeing actually behave. However, manually selecting combinations can tedious, work systematically, can challenging. solve problem, screen_limma_hyperparams() function written.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"using-screen_limma_hyperparams","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Using screen_limma_hyperparams()","title":"get-started","text":"function screen_limma_hyperparams() automates process testing different combinations hyperparameters. βs works: Specify values: hyperparameter, can specify values want test. Run combinations: function runs limma spline analysis combinations formed hyperparameters βve provided semi combinatorial way.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"inner-vs--outer-hyperparameters","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Inner vs.Β Outer Hyperparameters","title":"get-started","text":"Semi combinatorial means every possible combination generated. Instead, inner outer hyperparameters: possible combinations outer hyperparameters generated. version data (outer hyperparameter), combinations inner hyperparameters tested. approach neccessary, otherwise amount combos explode.","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"example","dir":"Articles","previous_headings":"Perform EDA (exploratory data analysis) > Finding the Best Hyperparameters","what":"Example","title":"get-started","text":"example, two versions dataset (one full dataset, one outliers removed), versions considered outer hyperparameters. Additionaly, lets say, want test two different limma design formulas, formula 1 2. function test combinations outer hyperparameters compare , results total 6 combinations : Full Dataset Formula 1 vs Full Dataset Formula 2 Full Dataset Formula 1 vs Outliers Removed Dataset Formula 1 Full Dataset Formula 1 vs Outliers Removed Dataset Formula 2 Full Dataset Formula 2 vs Outliers Removed Dataset Formula 1 Full Dataset Formula 2 vs Outliers Removed Dataset Formula 2 Outliers Removed Dataset Formula 1 vs Outliers Removed Dataset Formula 2 Letβs say specified following inner hyperparameters: Spline parameters: Natural cubic splines degree freedom either 2 3. Adjusted p-value threshold: 0.05 0.1. function generate test combinations spline parameters p-value thresholds 4 combos: Combo 1: DoF = 2, threshold = 0.05 DoF = 3, threshold = 0.05 DoF = 2, threshold = 0.1 DoF = 3, threshold = 0.1 Combo 2: DoF = 2, threshold = 0.05 DoF = 3, threshold = 0.05 DoF = 2, threshold = 0.1 DoF = 3, threshold = 0.1 Combo 3: β¦ allows systematically explore different combinations select optimal hyperparameters analysis. example proteomics data: Now specified values hyperparameter want test, can run screen_limma_hyperparams() function. mentioned, function generates report comparison outer hyperparameters, many show . can view example report report contains results comparison βouterβ hyperparameters data 1 design (formula) 1 compared data 1 design 2. , combinations βinnerβ hyperparameters generated (every possible combination specified adj. p-value thresholds spline configs). encoding used reports titles (part output screen_limma_hyperparams function).","code":"data1 <- data meta1 <- meta # Remove the \"outliers\" data2 <- data[, !(colnames(data) %in% c( \"E12_TP05_Exponential\", \"E10_TP10_Stationary\" ) )] # Adjust meta so that it matches data2 meta2 <- meta[!meta$Sample.ID %in% c( \"E12_TP05_Exponential\", \"E10_TP10_Stationary\" ), ] # As mentioned above, all the values of one hyperparameter are stored # and provided as a list. datas <- list(data1, data2) # This will be used to describe the versions of the data. datas_descr <- c( \"full_data\", \"outliers_removed\" ) metas <- list(meta1, meta2) # Test two different limma designs designs <- c( \"~ 1 + Phase*X + Reactor\", \"~ 1 + X + Reactor\" ) # 'Integrated means' limma will use the full dataset to generate the results for # each condition. 'Isolated' means limma will use only the respective part of # the dataset for each condition. Designs that contain the condition column # (here Phase) must have mode 'integrated', because the full data is needed to # include the different conditions into the design formula. modes <- c( \"integrated\", \"isolated\" ) # Specify the meta \"level\" column condition <- \"Phase\" report_dir <- here::here( \"results\", \"hyperparams_screen_reports\" ) # To remove the batch effect meta_batch_column <- \"Reactor\" # Test out two different p-value thresholds (inner hyperparameter) adj_pthresholds <- c( 0.05, 0.1 ) # Create a dataframe with combinations of spline parameters to test # (every row a combo to test) spline_test_configs <- data.frame( # 'n' stands for natural cubic splines, b for B-splines. spline_type = c(\"n\", \"n\", \"b\", \"b\"), # Degree is not applicable (NA) for natural splines. degree = c(NA, NA, 2L, 4L), # Degrees of freedom (DoF) to test. # Higher dof means spline can fit more complex patterns. dof = c(2L, 3L, 3L, 4L) ) print(spline_test_configs) #> spline_type degree dof #> 1 n NA 2 #> 2 n NA 3 #> 3 b 2 3 #> 4 b 4 4 SplineOmics::screen_limma_hyperparams( splineomics = splineomics, datas = datas, datas_descr = datas_descr, metas = metas, designs = designs, modes = modes, spline_test_configs = spline_test_configs, report_dir = report_dir, adj_pthresholds = adj_pthresholds, )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"run-limma-spline-analysis","dir":"Articles","previous_headings":"","what":"Run limma spline analysis","title":"get-started","text":"identified hyperparameters likely best ones, can run limma spline analysis get results. Lets just assume now new parameters, SplineOmics object updated, best analysis. choice depends analysis. example, analysis, natural cubic splines (n) dof two seemed fit data best (overfitting, also underfitting), reason spline parameters chosen. design formula, must specify either βisolatedβ βintegratedβ. Isolated means limma determines results level using data level. Integrated means limma determines results levels using full dataset (levels). integrated mode, condition column (Phase) must included design. Isolated means limma uses part dataset belongs level obtain results level. generate limma result categories 2 3 () Run run_limma_splines() function updated SplineOmics object: output function run_limma_splines() named list, element specific βcategoryβ results. Refer document explanation different result categories. elements list, containing elements respective limma topTables, either level comparison two levels. element βtime_effectβ list, element topTable p-value feature respective level reported. element βavrg_diff_conditionsβ list contains elements topTables, represent comparison average differences levels. element βinteraction_condition_timeβ list contains elements topTables, represent interaction levels (includes time average differences)","code":"splineomics <- SplineOmics::update_splineomics( splineomics = splineomics, design = \"~ 1 + Phase*X + Reactor\", # best design formula mode = \"integrated\", # means limma uses the full data for each condition. data = data2, # data without \"outliers\" was better meta = meta2, spline_params = list( spline_type = c(\"n\"), # natural cubic splines (take these if unsure) dof = c(2L) # If you are unsure about which dof, start with 2 and increase ) ) splineomics <- SplineOmics::run_limma_splines( splineomics = splineomics ) #> Column 'Reactor' of meta will be used to remove the batch effect for the plotting #> Make sure that the design formula contains no interaction between the condition and time (X) for mode == isolated, and that it contains an interaction for mode == integrated. Otherwise, you will get an uncaught error of 'coefficients not estimable' or 'subscript out of bounds'. #> Info limma spline analysis completed successfully"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"build-limma-report","dir":"Articles","previous_headings":"","what":"Build limma report","title":"get-started","text":"topTables three limma result categories can used generate p-value histograms volcano plots. can view generated analysis report create_limma_report function . report contains p-value histograms three limma result categories volcano plot category 2. Embedded file downloadable limma topTables results category 1 mode βisolatedβ also results category 2 3 mode βintegratedβ. Note upcoming cluster_hits() function report, embedded file contain clustered significant features result category 1.","code":"report_dir <- here::here( \"results\", \"create_limma_reports\" ) plots <- SplineOmics::create_limma_report( splineomics = splineomics, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"cluster-the-hits-significant-features","dir":"Articles","previous_headings":"","what":"Cluster the hits (significant features)","title":"get-started","text":"obtained limma spline results, can cluster hits based temporal pattern (spline shape). define hit setting adj. p-value threshold every level. Hits features (e.g.Β proteins) adj. p-value threshold. Hierarchical clustering used place every hit one many clusters specified specific level. can view generated analysis report cluster_hits function . discussed , three limma result categories. cluster_hits() report shows results three, present (category 2 3 can generated design formula contains interaction effect).","code":"adj_pthresholds <- c( # 0.05 for both levels 0.05, # exponential 0.05 # stationary ) clusters <- c( 6L, # 6 clusters for the exponential phase level 3L # 3 clusters for the stationary phase level ) report_dir <- here::here( \"results\", \"clustering_reports\" ) plot_info <- list( # For the spline plots y_axis_label = \"log2 intensity\", time_unit = \"min\", # our measurements were in minutes treatment_labels = list(\"feeding\"), # add this for all conditions treatment_timepoints = list(0) # Feeding was at 0 minutes. ) # Like this you can add individual treatment labels to your plots: # treatment_labels = list( # exponential = \"treatment 1\", # One treatment in exp # stationary = c(\"treatment 2\", \"treatment 3\"), # Two treatments in stat # additional_condition = NA # No treatment in the hypothetical third condition # ) # # treatment_timepoints = list( # exponential = 0, # stationary = c(100, 140), # Two treatments also need two timepoints # additional_condition = NA # ) # # or set a treatment for ALL conditions (still always make a list): # # treatment_labels = list(\"treatment\") # treatment_timepoints = list(120) # # or set multiple treatments for ALL conditions: # # treatment_labels = list(c(\"treatment1\", \"treatment2\")) # treatment_timepoints = list(c(120, 90)) # Get all the gene names. They are used for generating files # which contents can be directly used as the input for the Enrichr webtool, # if you prefer to manually perform the enrichment. Those files are # embedded in the output HTML report and can be downloaded from there. gene_column_name <- \"Gene_symbol\" genes <- annotation[[gene_column_name]] plot_options <- list( # When meta_replicate_column is not there, all datapoints are blue. meta_replicate_column = \"Reactor\", # Colors the data points based on Reactor cluster_heatmap_columns = FALSE # Per default FALSE, just for demonstration ) clustering_results <- SplineOmics::cluster_hits( splineomics = splineomics, adj_pthresholds = adj_pthresholds, clusters = clusters, genes = genes, plot_info = plot_info, plot_options = plot_options, report_dir = report_dir, adj_pthresh_avrg_diff_conditions = 0, adj_pthresh_interaction_condition_time = 0.25 )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"perform-gene-set-enrichment-analysis-gsea","dir":"Articles","previous_headings":"","what":"Perform gene set enrichment analysis (GSEA)","title":"get-started","text":"clustered hits identified, subsequent step gain biological insights perform GSEA. , respective genes can assigned clustered hit, GSEA can carried . proceed, Enrichr databases choice need downloaded: Per default file placed current working directory, root dir R project. run GSEA, downloaded database file loaded dataframe. , optionally, clusterProfiler parameters report dir can specified. function create_gsea_report() runs GSEA using clusterProfiler, generates HTML report returns GSEA dotplots R. function runs clusterProfiler clusters levels, generates HTML report: can view generated analysis report run_gsea function . report first shows enrichment results, 2 genes supported term, tabular format. table terms < 2 genes supporting can downloaded clicking button table. dotplots , every row term specific database, columns respective clusters. color scale contains info odds ratio size -log10 adj. p-value. terms > 2 genes support included plot. , cluster, just maximally 5 terms shown (terms highest odds ratios). Note example cluster 1 already 5 terms, cluster 2 , gets term also found cluster 1, term included sixth term cluster 1, way maximum 5 can exceeded. phase, like stationary , lead enrichment results, stated red message.","code":"# Specify which databases you want to download from Enrichr gene_set_lib <- c( \"WikiPathways_2019_Human\", \"NCI-Nature_2016\", \"TRRUST_Transcription_Factors_2019\", \"MSigDB_Hallmark_2020\", \"GO_Cellular_Component_2018\", \"CORUM\", \"KEGG_2019_Human\", \"TRANSFAC_and_JASPAR_PWMs\", \"ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\", \"GO_Biological_Process_2018\", \"GO_Molecular_Function_2018\", \"Human_Gene_Atlas\" ) SplineOmics::download_enrichr_databases( gene_set_lib = gene_set_lib, filename = \"databases.tsv\" ) # Specify the filepath of the TSV file with the database info downloaded_dbs_filepath <- here::here(\"databases.tsv\") # Load the file databases <- read.delim( downloaded_dbs_filepath, sep = \"\\t\", stringsAsFactors = FALSE ) # Specify the clusterProfiler parameters clusterProfiler_params <- list( pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2 ) report_dir <- here::here( \"results\", \"gsea_reports\" ) result <- SplineOmics::run_gsea( # A dataframe with three columns: feature, cluster, and gene. Feature contains # the integer index of the feature, cluster the integer specifying the cluster # number, and gene the string of the gene, such as \"CLSTN2\". levels_clustered_hits = clustering_results$clustered_hits_levels, databases = databases, clusterProfiler_params = clusterProfiler_params, report_info = report_info, report_dir = report_dir )"},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"get-started","text":"example showed functionalities SplineOmics package. can also run datatypes , including timeseries RNA-seq glycan data (, refer documentation README file GitHub page Usage/RNA-seq Glycan Data). get interactive version example, download SplineOmics package run function open_tutorial() opens R Markdown file, can run different code blocks working R Studio (recommendet) can easily check values individual variables generate output reports . run function open_template() get minimal R Markdown file, code written can use skeleton plug data run . hope SplineOmics package makes scientific data analysis easier. face problems (bugs code) satisfied documentation, open issue GitHub check options Feedback section README GitHub. Thank !","code":""},{"path":"https://csbg.github.io/SplineOmics/articles/get-started.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"get-started","text":"","code":"#> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/local/R-4.3.3/lib/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8 #> [5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8 #> [7] LC_PAPER=de_AT.UTF-8 LC_NAME=C #> [9] LC_ADDRESS=C LC_TELEPHONE=C #> [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Europe/Vienna #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices datasets utils methods base #> #> other attached packages: #> [1] dplyr_1.1.4 here_1.0.1 readxl_1.4.3 SplineOmics_0.1.0 #> #> loaded via a namespace (and not attached): #> [1] bitops_1.0-9 Rdpack_2.6.1 gridExtra_2.3 #> [4] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65 #> [7] GetoptLong_1.0.5 matrixStats_1.4.1 compiler_4.3.3 #> [10] reshape2_1.4.4 png_0.1-8 systemfonts_1.1.0 #> [13] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 #> [16] shape_1.4.6.1 crayon_1.5.3 fastmap_1.2.0 #> [19] backports_1.5.0 caTools_1.18.3 utf8_1.2.4 #> [22] rmarkdown_2.28 nloptr_2.1.1 ragg_1.3.3 #> [25] purrr_1.0.2 xfun_0.48 cachem_1.1.0 #> [28] jsonlite_1.8.9 progress_1.2.3 EnvStats_3.0.0 #> [31] remaCor_0.0.18 BiocParallel_1.36.0 broom_1.0.7 #> [34] parallel_4.3.3 prettyunits_1.2.0 cluster_2.1.6 #> [37] R6_2.5.1 bslib_0.8.0 stringi_1.8.4 #> [40] RColorBrewer_1.1-3 limma_3.58.1 boot_1.3-29 #> [43] cellranger_1.1.0 numDeriv_2016.8-1.1 jquerylib_0.1.4 #> [46] Rcpp_1.0.13 iterators_1.0.14 knitr_1.48 #> [49] base64enc_0.1-3 IRanges_2.36.0 Matrix_1.6-5 #> [52] splines_4.3.3 tidyselect_1.2.1 rstudioapi_0.16.0 #> [55] yaml_2.3.10 viridis_0.6.5 doParallel_1.0.17 #> [58] gplots_3.2.0 codetools_0.2-19 plyr_1.8.9 #> [61] lmerTest_3.1-3 lattice_0.22-5 tibble_3.2.1 #> [64] withr_3.0.1 Biobase_2.62.0 evaluate_1.0.0 #> [67] desc_1.4.3 zip_2.3.1 circlize_0.4.16 #> [70] pillar_1.9.0 BiocManager_1.30.25 KernSmooth_2.23-22 #> [73] renv_1.0.10 foreach_1.5.2 stats4_4.3.3 #> [76] generics_0.1.3 rprojroot_2.0.4 S4Vectors_0.40.2 #> [79] hms_1.1.3 ggplot2_3.5.1 munsell_0.5.1 #> [82] scales_1.3.0 aod_1.3.3 minqa_1.2.8 #> [85] gtools_3.9.5 RhpcBLASctl_0.23-42 glue_1.8.0 #> [88] pheatmap_1.0.12 tools_4.3.3 fANCOVA_0.6-1 #> [91] dendextend_1.18.0 variancePartition_1.32.5 lme4_1.1-35.5 #> [94] openxlsx_4.2.7.1 mvtnorm_1.3-1 fs_1.6.4 #> [97] grid_4.3.3 tidyr_1.3.1 rbibutils_2.3 #> [100] colorspace_2.1-1 nlme_3.1-164 patchwork_1.3.0 #> [103] cli_3.6.3 textshaping_0.4.0 fansi_1.0.6 #> [106] viridisLite_0.4.2 svglite_2.1.3 ComplexHeatmap_2.18.0 #> [109] corpcor_1.6.10 gtable_0.3.5 sass_0.4.9 #> [112] digest_0.6.37 BiocGenerics_0.48.1 pbkrtest_0.5.3 #> [115] ggrepel_0.9.6 rjson_0.2.23 htmlwidgets_1.6.4 #> [118] farver_2.1.2 htmltools_0.5.8.1 pkgdown_2.1.1 #> [121] lifecycle_1.0.4 GlobalOptions_0.1.2 statmod_1.5.0 #> [124] MASS_7.3-60.0.1"},{"path":"https://csbg.github.io/SplineOmics/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Thomas Rauter. Author, maintainer.","code":""},{"path":"https://csbg.github.io/SplineOmics/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Rauter T (2025). SplineOmics: Streamlines process analysing omics timeseries data splines. R package version 0.1.0, https://csbg.github.io/SplineOmics.","code":"@Manual{, title = {SplineOmics: Streamlines the process of analysing omics timeseries data with splines}, author = {Thomas Rauter}, year = {2025}, note = {R package version 0.1.0}, url = {https://csbg.github.io/SplineOmics}, }"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"splineomics","dir":"","previous_headings":"","what":"Streamlines the process of analysing omics timeseries data with splines","title":"Streamlines the process of analysing omics timeseries data with splines","text":"R package SplineOmics finds significant features (hits) time-series -omics data using splines limma hypothesis testing. clusters hits based spline shape showing results summary HTML reports. graphical abstract shows full workflow streamlined SplineOmics: Graphical Abstract SplineOmics Workflow","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of Contents","title":"Streamlines the process of analysing omics timeseries data with splines","text":"π Introduction π³ Docker Container Tutorial Details RNA-seq Glycan Data π¦ Dependencies π Reading β Getting Help π€ Contributing π¬ Feedback π License π Citation π Contributors π Acknowledgements","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-introduction","dir":"","previous_headings":"","what":"π Introduction","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Welcome SplineOmics, R package designed streamline analysis -omics time-series data, followed automated HTML report generation.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"is-the-splineomics-package-of-use-for-me","dir":"","previous_headings":"π Introduction","what":"Is the SplineOmics package of use for me?","title":"Streamlines the process of analysing omics timeseries data with splines","text":"-omics data time, package help run limma splines, decide parameters use, perform clustering, run GSEA show result plots HTML reports. time-series data valid input limma package also valid input SplineOmics package (transcriptomics, proteomics, phosphoproteomics, metabolomics, glycan fractional abundances, etc.).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"what-do-i-need-precisely","dir":"","previous_headings":"π Introduction","what":"What do I need precisely?","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Data: data matrix row feature (e.g., protein, metabolite, etc.) column sample taken specific time. data must NA values, normally distributed features dependence samples. Meta: table metadata columns/samples data matrix (e.g., batch, time point, etc.) Annotation (optional): table identifiers rows/features data matrix (e.g., gene protein name).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"capabilities","dir":"","previous_headings":"π Introduction","what":"Capabilities","title":"Streamlines the process of analysing omics timeseries data with splines","text":"SplineOmics, can: Automatically perform exploratory data analysis: explore_data() function generates HTML report, containing various plots, density, PCA, correlation heatmap plots (example report). Explore various limma splines hyperparameters: Test combinations hyperparameters, different datasets, limma design formulas, degrees freedom, p-value thresholds, etc., using screen_limma_hyperparams() function (example report (along encoding)). Perform limma spline analysis: Use run_limma_splines() function perform limma analysis splines optimal hyperparameters identified (example report). Cluster significant features: Cluster significant features (hits) identified spline analysis cluster_hits() function (example report). Run GSEA clustered hits: Perform gene set enrichment analysis (GSEA) using clustered hits create_gsea_report() function (example report).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-installation","dir":"","previous_headings":"","what":"π§ Installation","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Follow steps install SplineOmics package GitHub repository R environment.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"prerequisites","dir":"","previous_headings":"π§ Installation","what":"Prerequisites","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Ensure R installed system. , download install CRAN. RStudio recommended user-friendly experience R. Download install RStudio posit.co.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"installation-steps","dir":"","previous_headings":"π§ Installation","what":"Installation Steps","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Note Windows Users: Note installation paths potentially writable Windows. Therefore, can necessary set library path use path installations: Alternatively, can run RStudio administrator installation (however generally recommended, security risk). Open RStudio R console. Install BiocManager Bioconductor dependencies (already installed) Install Bioconductor dependencies separately using BiocManager Install remotes package GitHub downloads (already installed) Install SplineOmics package GitHub non-Bioconductor dependencies, using remotes Verify installation SplineOmics package","code":"custom_lib_path <- \"C:/Rlibs\" # Replace with your desired path # Create the directory if it doesn't exist if (!dir.exists(custom_lib_path)) { dir.create( custom_lib_path, showWarnings = FALSE, recursive = TRUE ) } # Set the library path to include the new directory .libPaths(c(custom_lib_path, .libPaths())) # Check if the new library path is added successfully if (custom_lib_path %in% .libPaths()) { message(\"Library path set to: \", custom_lib_path) } else { stop(\"Failed to set library path.\") } install.packages( \"BiocManager\" # lib = custom_lib_path ) BiocManager::install( c(\"ComplexHeatmap\", \"limma\") # force = TRUE # when encountering issues # lib = custom_lib_path ) install.packages( \"remotes\" # lib = custom_lib_path ) remotes::install_github( \"csbg/SplineOmics\", # GitHub repository ref = \"0.1.0\", # Specify the tag to install dependencies = TRUE, # Install all dependencies upgrade = \"always\" # Always upgrade dependencies # force = TRUE # when encountering issues # lib = custom_lib_path ) # Verify the installation of the SplineOmics package if (\"SplineOmics\" %in% rownames(installed.packages())) { message(\"SplineOmics was installed successfully.\") } else { message(\"SplineOmics installation failed. Please check for errors during installation.\") }"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"troubleshooting","dir":"","previous_headings":"π§ Installation","what":"Troubleshooting","title":"Streamlines the process of analysing omics timeseries data with splines","text":"encounter errors related dependencies package versions installation, try updating R RStudio latest versions repeat installation steps. issues specifically related SplineOmics package, check Issues section GitHub repository similar problems post new issue.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-docker-container","dir":"","previous_headings":"π§ Installation","what":"π³ Docker Container","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Alternatively, can run analysis Docker container. underlying Docker image encapsulates SplineOmics package together necessary environment dependencies. ensures higher levels reproducibility analysis carried consistent environment, independent operating system custom configurations. Please note must Docker Engine installed machine. instructions install , consult official Docker Engine installation guide. information Docker containers can found official Docker page. instructions downloading image SplineOmics package running container, please refer Docker instructions.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"troubleshooting-1","dir":"","previous_headings":"π§ Installation > π³ Docker Container","what":"Troubleshooting","title":"Streamlines the process of analysing omics timeseries data with splines","text":"face βpermission deniedβ issues Linux distributions, check vignette.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"tutorial","dir":"","previous_headings":"βΆ Usage","what":"Tutorial","title":"Streamlines the process of analysing omics timeseries data with splines","text":"tutorial covers real CHO cell time-series proteomics example start end. open R Markdown file tutorial RStudio, run: open R Markdown file RStudio containing template analysis, run:","code":"library(SplineOmics) open_tutorial() library(SplineOmics) open_template()"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"details","dir":"","previous_headings":"βΆ Usage","what":"Details","title":"Streamlines the process of analysing omics timeseries data with splines","text":"detailed description arguments outputs functions package (exported internal functions) can found .","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"design-limma-design-formula","dir":"","previous_headings":"βΆ Usage > Details","what":"Design limma design formula","title":"Streamlines the process of analysing omics timeseries data with splines","text":"quick guide design limma design formula can found explanation three different limma results ","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"rna-seq-data","dir":"","previous_headings":"βΆ Usage > RNA-seq and Glycan Data","what":"RNA-seq data","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Transcriptomics data must preprocessed limma. need provide appropriate object, voom object, rna_seq_data argument SplineOmics object (see documentation). Along , normalized matrix (e.g., $E slot voom object) must passed data argument. allows flexibility preprocessing; can use method prefer long final object matrix compatible limma. One way preprocess RNA-seq data using preprocess_rna_seq_data() function included SplineOmics package (see documentation).","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"glycan-fractional-abundance-data","dir":"","previous_headings":"βΆ Usage > RNA-seq and Glycan Data","what":"Glycan fractional abundance data","title":"Streamlines the process of analysing omics timeseries data with splines","text":"glycan fractional abundance data matrix, row represents type glycan columns correspond timepoints, must transformed analysis. preprocessing step essential due compositional nature data. compositional data, increase abundance one component (glycan) necessarily results decrease others, introducing dependency among variables can bias analysis. One way address issue applying Centered Log Ratio (CLR) transformation data clr function compositions package: results clr transformed data can harder understand interpret however. prefer ease interpretation fine results contain artifacts due compositional nature data, log2 transform data instead use input SplineOmics package.","code":"library(compositions) clr_transformed_data <- clr(data_matrix) # use as SplineOmics input log2_transformed_data <- log2(data_matrix) # use as SplineOmics input"},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-dependencies","dir":"","previous_headings":"","what":"π¦ Dependencies","title":"Streamlines the process of analysing omics timeseries data with splines","text":"SplineOmics package relies several R packages functionality. list dependencies automatically installed along SplineOmics. already packages installed, ensure date avoid compatibility issues. ComplexHeatmap (>= 2.18.0): creating complex heatmaps advanced features. base64enc (>= 0.1-3): encoding/decoding base64. dendextend (>= 1.17.1): extending dendrogram objects, allowing easier manipulation dendrograms. dplyr (>= 1.1.4): data manipulation. ggplot2 (>= 3.5.1): creating elegant data visualizations using grammar graphics. ggrepel (>= 0.9.5): better label placement ggplot2. (>= 1.0.1): constructing paths projectβs files. limma (>= 3.58.1): linear models microarray RNA-seq analysis. openxlsx (>= 4.2.6.1): reading, writing, editing .xlsx files. patchwork (>= 1.2.0): combining multiple ggplot objects single plot. pheatmap (>= 1.0.12): creating aesthetically pleasing heatmaps. progress (>= 1.2.3): adding progress bars loops apply functions. purrr (>= 1.0.2): functional programming tools. rlang (>= 1.1.4): working core language features R. scales (>= 1.3.0): scale functions visualizations. svglite (>= 2.1.3): creating high-quality vector graphics (SVG). tibble (>= 3.2.1): creating tidy data frames. tidyr (>= 1.3.1): tidying data. zip (>= 2.3.1): compressing combining files zip archives.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"optional-dependencies","dir":"","previous_headings":"π¦ Dependencies","what":"Optional Dependencies","title":"Streamlines the process of analysing omics timeseries data with splines","text":"packages optional needed specific functionality: edgeR (>= 4.0.16): preprocessing RNA-seq data preprocess_rna_seq_data() function. clusterProfiler (>= 4.10.1): run_gsea() function (gene set enrichment analysis). rstudioapi (>= 0.16.0): open_tutorial() open_template() functions.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"r-version","dir":"","previous_headings":"π¦ Dependencies","what":"R Version","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Recommended: R 4.3.3 higher","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-further-reading","dir":"","previous_headings":"","what":"π Further Reading","title":"Streamlines the process of analysing omics timeseries data with splines","text":"interested gaining deeper understanding methodologies used SplineOmics package, recommended publications: Splines: learn splines, can refer review. limma: read limma R package, can refer publication. PCA: learn PCA, download read document. Hierarchical clustering: get information hierarchical clustering, can refer web article.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-getting-help","dir":"","previous_headings":"","what":"β Getting Help","title":"Streamlines the process of analysing omics timeseries data with splines","text":"encounter bug suggestion improving SplineOmics package, encourage open issue GitHub repository. opening new issue, please check see question bug already reported another user. helps avoid duplicate reports ensures can address problems efficiently. detailed questions, discussions, contributions regarding packageβs use development, please refer GitHub Discussions page SplineOmics.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-contributing","dir":"","previous_headings":"","what":"π€ Contributing","title":"Streamlines the process of analysing omics timeseries data with splines","text":"welcome contributions SplineOmics package! Whether βre interested fixing bugs, adding new features, improving documentation, help greatly appreciated. βs can contribute: Report Bug Request Feature: encounter bug idea new feature, please open issue GitHub repository. opening new issue, check see issue already reported feature requested another user. Submit Pull Request: βve developed bug fix new feature βd like share, submit pull request. Improve Documentation: Good documentation crucial project. notice missing incorrect documentation, please feel free contribute. Please adhere Code Conduct interactions project. Thank considering contributing SplineOmics. efforts make open-source community fantastic place learn, inspire, create.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-feedback","dir":"","previous_headings":"","what":"π¬ Feedback","title":"Streamlines the process of analysing omics timeseries data with splines","text":"appreciate feedback! Besides raising issues, can provide feedback following ways: Direct Email: Send feedback directly Thomas Rauter. Anonymous Feedback: Use Google Form provide anonymous feedback answering questions. feedback helps us improve project address issues may encounter.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-license","dir":"","previous_headings":"","what":"π License","title":"Streamlines the process of analysing omics timeseries data with splines","text":"package licensed MIT License: LICENSE Β© 2024 Thomas Rauter. rights reserved.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-citation","dir":"","previous_headings":"","what":"π Citation","title":"Streamlines the process of analysing omics timeseries data with splines","text":"SplineOmics package currently published peer-reviewed scientific journal similar outlet. However, package helped work, consider citing GitHub repository. cite package, can use citation information provided CITATION.cff file. can also generate citation various formats using CITATION.cff file visiting top right repo clicking βCite repositoryβ button. Also, like package, consider giving GitHub repository star. support helps us continued development improvement SplineOmics. Thank using package!","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-contributors","dir":"","previous_headings":"","what":"π Contributors","title":"Streamlines the process of analysing omics timeseries data with splines","text":"Thomas-Rauter - π Wrote package, developed approach together VSchaepertoens guidance nfortelny skafdasschaf. nfortelny - π§ Principal Investigator, provided guidance support overall approach. skafdasschaf - π§ Helped reviewing code, delivered improvement suggestions scientific guidance develop approach. VSchaepertoens - β¨ Developed one internal plotting function, well code exploratory data analysis plots, overall approach together Thomas-Rauter.","code":""},{"path":"https://csbg.github.io/SplineOmics/index.html","id":"id_-acknowledgements","dir":"","previous_headings":"","what":"π Acknowledgements","title":"Streamlines the process of analysing omics timeseries data with splines","text":"work carried context DigiTherapeutX project, funded Austrian Science Fund (FWF). research conducted supervision Prof.Β Nikolaus Fortelny, leads Computational Systems Biology working group Paris Lodron University Salzburg, Austria. can find information Prof.Β Fortelnyβs research group .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":null,"dir":"Reference","previous_headings":"","what":"InputControl: A class for controlling and validating inputs β InputControl","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"InputControl: class controlling validating inputs InputControl: class controlling validating inputs","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"class provides methods validate inputs function. method performs following checks: * Ensures `annotation` `data` provided. * Confirms `annotation` dataframe. * Verifies `annotation` `data` number rows. checks fail, informative error message returned. function performs following checks: - `clusters` integer vector integers. Otherwise, gives error. Check Plot Info method performs following checks: * Ensures `plot_info` provided NULL. * Confirms `y_axis_label` character vector maximally 30 characters. * Confirms `time_unit` character vector maximally 15 characters. * Validates `treatment_labels` either `NA` character vector element maximally 15 characters long. * Validates `treatment_timepoints` either `NA` numeric vector length `treatment_labels` `treatment_labels` `NA`. checks fail, informative error message returned. function performs following checks: 1. Ensures `feature_name_columns` `annotation` `NULL`. 2. Verifies element `feature_name_columns` character length 1. 3. Checks elements `feature_name_columns` valid column names `annotation` data frame. Check Report function performs following checks: - Whether `report` argument present. - `report` Boolean value (`TRUE` `FALSE`), throws error.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"InputControl: method verifies spline test configurations associated metadata within object's arguments. performs series checks configurations, including column verification, spline type validation, ensuring degrees freedom (dof) within acceptable ranges.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"SplineOmics::Level4Functions -> SplineOmics::Level3Functions -> SplineOmics::Level2Functions -> InputControl","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"args list arguments validated. Initialize InputControl object","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"SplineOmics::Level4Functions$create_error_message() SplineOmics::Level3Functions$check_batch_column() SplineOmics::Level3Functions$check_condition_time_consistency() SplineOmics::Level3Functions$check_voom_structure() SplineOmics::Level2Functions$check_columns() SplineOmics::Level2Functions$check_columns_spline_test_configs() SplineOmics::Level2Functions$check_data() SplineOmics::Level2Functions$check_dataframe() SplineOmics::Level2Functions$check_max_and_min_dof() SplineOmics::Level2Functions$check_meta() SplineOmics::Level2Functions$check_spline_params_generally() SplineOmics::Level2Functions$check_spline_params_mode_dependent() SplineOmics::Level2Functions$check_spline_type_column() SplineOmics::Level2Functions$check_spline_type_params()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"InputControl$new() InputControl$auto_validate() InputControl$check_data_and_meta() InputControl$check_annotation() InputControl$check_datas_and_metas() InputControl$check_datas_descr() InputControl$check_top_tables() InputControl$check_design_formula() InputControl$check_dream_params() InputControl$check_modes() InputControl$check_mode() InputControl$check_designs_and_metas() InputControl$check_spline_params() InputControl$check_spline_test_configs() InputControl$check_limma_top_tables() InputControl$check_adj_pthresholds() InputControl$check_adj_pthresh_limma_category_2_3() InputControl$check_clusters() InputControl$check_plot_info() InputControl$check_plot_options() InputControl$check_raw_data() InputControl$check_report_dir() InputControl$check_genes() InputControl$check_padjust_method() InputControl$check_report_info() InputControl$check_feature_name_columns() InputControl$check_report() InputControl$clone()","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$new(args)"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"args list arguments validated.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"new instance InputControl class. Automatically Validate Arguments method automatically validates arguments sequentially calling various validation methods defined within class. validation method checks specific aspects input arguments raises error validation fails. following validation methods called sequence: - self$check_data_and_meta() - self$check_datas_and_metas() - self$check_datas_descr() - self$check_design_formula() - self$check_mode() - self$check_modes() - self$check_designs_and_metas() - self$check_spline_params() - self$check_spline_test_configs() - self$check_adj_pthresholds() - self$check_clusters() - self$check_time_unit() - self$check_raw_data() - self$check_report_dir() - self$check_padjust_method() - self$check_report_info() - self$check_report() - self$check_feature_name_columns()","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$auto_validate()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL. function used side effects validating input arguments raising errors validation fails. Check Data Meta","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-data-and-meta-","dir":"Reference","previous_headings":"","what":"Method check_data_and_meta()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity data meta objects, ensuring data matrix numeric values meta dataframe containing specified condition column. Additionally, verifies number columns data matrix matches number rows meta dataframe.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_data_and_meta()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"data matrix containing numeric values. meta dataframe containing metadata, including 'Time' column specified condition column. condition single character string specifying column name meta dataframe checked. meta_batch_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. data_meta_index optional parameter specifying index data/meta pair error messages. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check Annotation Consistency","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-annotation-","dir":"Reference","previous_headings":"","what":"Method check_annotation()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"method checks consistency annotation data. ensures annotation dataframe number rows data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_annotation()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL required arguments missing. Otherwise, performs checks potentially raises errors checks fail. Check Multiple Data Meta Pairs","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-datas-and-metas-","dir":"Reference","previous_headings":"","what":"Method check_datas_and_metas()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Iterates multiple data meta pairs validate pair using `check_data_and_meta` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_datas_and_metas()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"datas list matrices containing numeric values. metas list data frames containing metadata. condition character string specifying column name meta dataframe checked. meta_batch_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. meta_batch2_column optional parameter specifying column name meta dataframe used remove second batch effect. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-4","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL check fails, otherwise returns TRUE. Check Data Descriptions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-datas-descr-","dir":"Reference","previous_headings":"","what":"Method check_datas_descr()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates data descriptions character vectors element exceeding 80 characters length.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_datas_descr()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"datas_descr character vector data descriptions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-5","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-top-tables-","dir":"Reference","previous_headings":"","what":"Method check_top_tables()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates top tables list dataframes checks dataframe using `check_dataframe` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_top_tables()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"top_tables list top tables limma analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-6","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"return value, called side effects. Check Design Formula","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-design-formula-","dir":"Reference","previous_headings":"","what":"Method check_design_formula()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates design formula ensuring valid character string, contains allowed characters, includes intercept term 'X', references columns present metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_design_formula()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"formula character string representing design formula. meta data frame containing metadata. meta_index optional index data/meta pair.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-7","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"TRUE design formula valid, otherwise error thrown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-dream-params-","dir":"Reference","previous_headings":"","what":"Method check_dream_params()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity `dream_params` argument provided class. `dream_params` present, ensures contains required optional elements correct format. Specifically, `dream_params` must contain named element `random_effects`, required must string. may also optionally contain elements `dof`, must integer greater 1, `KenwardRoger`, must boolean. Unnamed elements elements three allowed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_dream_params()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-8","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `TRUE` `dream_params` passes checks. Otherwise, stops function returns error message using `stop_call_false`. Validate check modes","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-modes-","dir":"Reference","previous_headings":"","what":"Method check_modes()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function iterates `modes` argument, sets `mode` `self$args`, calls `check_mode()` validate mode. validation, `mode` removed `self$args`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_modes()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-9","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL `modes` missing; otherwise, checks modes. Check mode argument validity","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-mode-","dir":"Reference","previous_headings":"","what":"Method check_mode()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks `mode` argument provided validates either \"isolated\" \"integrated\". `mode` missing invalid, error thrown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_mode()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-10","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL `mode` missing; otherwise, validates mode. Check Multiple Designs Metas","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-designs-and-metas-","dir":"Reference","previous_headings":"","what":"Method check_designs_and_metas()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Iterates multiple design formulas corresponding metadata validate pair using `check_design_formula` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-11","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_designs_and_metas()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"designs vector character strings representing design formulas. metas list data frames containing metadata. meta_indices vector optional indices data/meta pairs.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-11","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL check fails, otherwise returns TRUE. Check Spline Parameters","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-spline-params-","dir":"Reference","previous_headings":"","what":"Method check_spline_params()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates spline parameters generally depending specified mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-12","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_spline_params()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"spline_params list spline parameters. mode character string specifying mode ('integrated' 'isolated'). meta dataframe containing metadata. condition character string specifying condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-12","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `NULL` required arguments mising, otherwise, called side effects. Check Spline Test Configurations","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-13","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_spline_test_configs()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"spline_test_configs configuration object spline tests. metas list metadata corresponding data matrices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-13","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `NULL` required arguments mising, otherwise, called side effects. Check Limma Top Tables Structure function checks provided limma top tables data structure correctly formatted. ensures data structure contains exactly three named elements ('time_effect', 'avrg_diff_conditions', 'interaction_condition_time') element contains dataframes correct columns data types.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-14","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_limma_top_tables()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"self object containing data structure check.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-14","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function return value. stops execution data structure match expected format. Check Adjusted p-Thresholds","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-adj-pthresholds-","dir":"Reference","previous_headings":"","what":"Method check_adj_pthresholds()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity adjusted p-thresholds vector, ensuring elements numeric, greater 0, less 1. conditions met, function stops execution returns error message indicating offending elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-15","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_adj_pthresholds()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"adj_pthresholds numeric vector adjusted p-thresholds.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-15","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check adjusted p-value thresholds limma category 2 3","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-adj-pthresh-limma-category-","dir":"Reference","previous_headings":"","what":"Method check_adj_pthresh_limma_category_2_3()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks adjusted p-value thresholds average difference conditions interaction condition time non-null, floats, range [0, 1].","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-16","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_adj_pthresh_limma_category_2_3()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-16","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"`NULL` either argument `NULL` invalid. Otherwise, return value (assumed valid inputs). Check Clusters","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-clusters-","dir":"Reference","previous_headings":"","what":"Method check_clusters()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function verifies cluster configurations within object's arguments. checks clusters argument present performs validation content. clusters specified, defaults automatic cluster estimation.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-17","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_clusters()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-plot-info-","dir":"Reference","previous_headings":"","what":"Method check_plot_info()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"method checks validity `plot_info` list. ensures `y_axis_label` `time_unit` meet length constraints, `treatment_labels` either `NA` character vector elements meeting length constraint, `treatment_timepoints` either `NA` numeric vector length `treatment_labels`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-18","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_plot_info()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-17","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"NULL `plot_info` provided invalid. Otherwise, performs checks potentially raises errors checks fail. Check plot options","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-plot-options-","dir":"Reference","previous_headings":"","what":"Method check_plot_options()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"method checks `plot_options` list contains required elements `meta_replicate_column` `cluster_heatmap_columns`. validates `cluster_heatmap_columns` either TRUE FALSE, `meta_replicate_column` valid column name `meta` dataframe. checks fail, script stops error message. Check Raw Data Validity","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-19","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_plot_options()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-raw-data-","dir":"Reference","previous_headings":"","what":"Method check_raw_data()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function verifies `raw_data` argument ensure numeric matrix dimensions `data`. NA values permitted within `raw_data`. `raw_data` numeric matrix match dimensions `data`, function stops execution error.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-20","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_raw_data()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-18","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns NULL either `data` `raw_data` provided checks pass. Stops execution raises error `raw_data` meet criteria. Check Create Report Directory","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-report-dir-","dir":"Reference","previous_headings":"","what":"Method check_report_dir()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks specified report directory exists valid directory. directory exist, attempts create . warnings errors directory creation, function stops execution returns error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-21","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_report_dir()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-11","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"report_dir character string specifying path report directory.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-19","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE directory exists successfully created. Stops execution returns error message directory created valid. Check Genes Validity","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-genes-","dir":"Reference","previous_headings":"","what":"Method check_genes()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks validity `data` `genes` arguments within `self$args` list. ensures `genes` character vector, neither `data` `genes` `NULL`, length `genes` matches number rows `data`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-22","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_genes()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-20","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `TRUE` checks pass. Returns `NULL` required arguments `NULL`. Throws error `genes` character vector length `genes` match number rows `data`. Check p-Adjustment Method","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-padjust-method-","dir":"Reference","previous_headings":"","what":"Method check_padjust_method()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks provided p-adjustment method valid. valid methods : \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"\", \"fdr\", \"none\". method one , function stops execution returns error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-23","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_padjust_method()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-12","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"padjust_method character string specifying p-adjustment method. Valid options \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"\", \"fdr\", \"none\".","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-21","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns TRUE p-adjustment method valid. Stops execution returns error message method invalid. Check Report Information","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-report-info-","dir":"Reference","previous_headings":"","what":"Method check_report_info()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Validates report information ensure contains mandatory fields adheres required formats.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-24","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_report_info()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-13","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"report_info named list containing report information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-22","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"TRUE report information valid; otherwise, error thrown. Check Feature Name Columns","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-feature-name-columns-","dir":"Reference","previous_headings":"","what":"Method check_feature_name_columns()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function checks whether elements `feature_name_columns` characters length 1 whether valid column names `annotation` data frame.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-25","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_feature_name_columns()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"returns-23","dir":"Reference","previous_headings":"","what":"Returns","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"Returns `NULL` required arguments missing. Throws error element `feature_name_columns` character length 1 element column name `annotation`. Returns `TRUE` checks pass.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-check-report-","dir":"Reference","previous_headings":"","what":"Method check_report()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"function verifies `report` argument within object's arguments. checks `report` argument present validates Boolean value.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-26","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$check_report()"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"usage-27","dir":"Reference","previous_headings":"","what":"Usage","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"","code":"InputControl$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/InputControl.html","id":"arguments-14","dir":"Reference","previous_headings":"","what":"Arguments","title":"InputControl: A class for controlling and validating inputs β InputControl","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Level2Functions: A class providing level 2 functionalities β Level2Functions","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Level2Functions: class providing level 2 functionalities Level2Functions: class providing level 2 functionalities","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"class provides various level 2 functionalities, including methods check dataframes spline parameters.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"SplineOmics::Level4Functions -> SplineOmics::Level3Functions -> Level2Functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"SplineOmics::Level4Functions$create_error_message() SplineOmics::Level3Functions$check_batch_column() SplineOmics::Level3Functions$check_condition_time_consistency() SplineOmics::Level3Functions$check_voom_structure()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Level2Functions$check_data() Level2Functions$check_meta() Level2Functions$check_dataframe() Level2Functions$check_spline_params_generally() Level2Functions$check_spline_params_mode_dependent() Level2Functions$check_columns_spline_test_configs() Level2Functions$check_spline_type_column() Level2Functions$check_spline_type_params() Level2Functions$check_max_and_min_dof() Level2Functions$check_columns() Level2Functions$clone()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-data-","dir":"Reference","previous_headings":"","what":"Method check_data()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"function checks validity data matrix, ensuring matrix, contains numeric values, missing values, elements non-negative. Additionally, verifies rows columns entirely zeros.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_data(data, data_meta_index = NULL)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"data dataframe containing numeric values. data_meta_index optional parameter specifying index data error messages. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check Metadata","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-meta-","dir":"Reference","previous_headings":"","what":"Method check_meta()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"function checks validity metadata dataframe, ensuring contains 'Time' column, contain missing values, specified condition column valid appropriate type. Additionally, checks optional batch effect column prints messages regarding use.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_meta( meta, condition, meta_batch_column = NULL, meta_batch2_column = NULL, data_meta_index = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"meta dataframe containing metadata, including 'Time' column. condition single character string specifying column name meta dataframe checked. meta_batch_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. meta_batch2_column optional parameter specifying column name meta dataframe used remove batch effect. Default NA. data_meta_index optional parameter specifying index data/meta pair error messages. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Returns TRUE checks pass. Stops execution returns error message check fails. Check Dataframe","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-dataframe-","dir":"Reference","previous_headings":"","what":"Method check_dataframe()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates dataframe contains required columns correct data types.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_dataframe(df)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"df dataframe check.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"TRUE dataframe valid, otherwise error thrown. Check Spline Parameters Generally","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-params-generally-","dir":"Reference","previous_headings":"","what":"Method check_spline_params_generally()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates general structure contents spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_params_generally(spline_params)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_params list spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects. Check Spline Parameters Mode Dependent","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-params-mode-dependent-","dir":"Reference","previous_headings":"","what":"Method check_spline_params_mode_dependent()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates spline parameters depending specified mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_params_mode_dependent( spline_params, mode, meta, condition )"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_params list spline parameters. mode character string specifying mode ('integrated' 'isolated'). meta dataframe containing metadata. condition character string specifying condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-4","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects. Check Columns Spline Test Configurations","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-columns-spline-test-configs-","dir":"Reference","previous_headings":"","what":"Method check_columns_spline_test_configs()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates spline test configurations contain required columns correct order.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_columns_spline_test_configs(spline_test_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-5","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-type-column-","dir":"Reference","previous_headings":"","what":"Method check_spline_type_column()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates 'spline_type' column spline test configurations contains 'n' 'b'.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_type_column(spline_test_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-6","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-spline-type-params-","dir":"Reference","previous_headings":"","what":"Method check_spline_type_params()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates parameters row spline test configurations based spline type.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_spline_type_params(spline_test_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-7","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"TRUE checks pass, otherwise error thrown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-check-max-and-min-dof-","dir":"Reference","previous_headings":"","what":"Method check_max_and_min_dof()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"Validates degrees freedom (DoF) row spline test configurations based metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_max_and_min_dof(spline_test_configs, metas)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"spline_test_configs dataframe containing spline test configurations. metas list metadata corresponding data matrices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-8","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$check_columns(df, expected_cols)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"df dataframe check. expected_cols character vector expected column names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"returns-9","dir":"Reference","previous_headings":"","what":"Returns","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"function return value. stops execution dataframe columns classes match expected structure.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"","code":"Level2Functions$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level2Functions.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level2Functions: A class providing level 2 functionalities β Level2Functions","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Level3Functions: A class for level 3 utility functions β Level3Functions","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Level3Functions: class level 3 utility functions Level3Functions: class level 3 utility functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"class provides methods creating error messages checking batch columns. function verifies `voom` object contains following components: - `E`: matrix log2-counts per million (logCPM) values. - `weights`: matrix observation-specific weights matches dimensions `E`. - `design`: matrix representing design matrix used linear modeling, number rows columns `E`. function also checks optional components : - `genes`: data frame gene annotations. - `targets`: data frame target information. - `sample.weights`: numeric vector sample-specific weights. checks fail, function stops reports issues. structure valid, message confirming validity printed.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"SplineOmics::Level4Functions -> Level3Functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"SplineOmics::Level4Functions$create_error_message()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Level3Functions$check_voom_structure() Level3Functions$check_batch_column() Level3Functions$check_condition_time_consistency() Level3Functions$clone()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-check-voom-structure-","dir":"Reference","previous_headings":"","what":"Method check_voom_structure()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"function checks structure `voom` object ensure contains expected components components correct types dimensions. function check actual data within matrices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$check_voom_structure(voom_obj)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"voom_obj list representing `voom` object, typically created `voom` function `limma` package.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Boolean TRUE FALSE. However, function mostly called side effects, stop script structure valid. Check Batch Column","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-check-batch-column-","dir":"Reference","previous_headings":"","what":"Method check_batch_column()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"method checks batch column metadata provides appropriate messages.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$check_batch_column(meta, meta_batch_column, data_meta_index)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"meta dataframe containing metadata. meta_batch_column character string specifying batch column metadata. data_meta_index optional parameter specifying index data/meta pair. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"NULL. method used side effects throwing errors printing messages. Check Condition Time Consistency","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-check-condition-time-consistency-","dir":"Reference","previous_headings":"","what":"Method check_condition_time_consistency()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"function checks whether values `condition` column unique values block identical `Time` values `meta` dataframe. Additionally, ensures every new block given time new value `condition` column.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$check_condition_time_consistency(meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"meta dataframe containing metadata, including `Time` column. condition character string specifying column name `meta` used define groups analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"Logical TRUE condition values consistent time series pattern.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"","code":"Level3Functions$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level3Functions.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level3Functions: A class for level 3 utility functions β Level3Functions","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Level4Functions: A class for level 3 utility functions β Level4Functions","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"Level4Functions: class level 3 utility functions Level4Functions: class level 3 utility functions","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"class provides methods creating error messages checking batch columns.","code":""},{"path":[]},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"Level4Functions$create_error_message() Level4Functions$clone()","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"method-create-error-message-","dir":"Reference","previous_headings":"","what":"Method create_error_message()","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"method creates formatted error message includes index data/meta pair provided. index provided, returns message .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"","code":"Level4Functions$create_error_message(message, data_meta_index = NULL)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"message character string specifying error message. data_meta_index optional parameter specifying index data/meta pair error message. Default NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"Returns formatted error message string. index provided, message includes index; otherwise, returns message .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"","code":"Level4Functions$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/Level4Functions.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Level4Functions: A class for level 3 utility functions β Level4Functions","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":null,"dir":"Reference","previous_headings":"","what":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"class provides methods identify upper-left lower-right cells numeric block within dataframe.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"data dataframe containing input data. upper_left_cell list containing row column indices upper-left cell. Initialize NumericBlockFinder object","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"NumericBlockFinder$new() NumericBlockFinder$find_upper_left_cell() NumericBlockFinder$find_lower_right_cell() NumericBlockFinder$clone()","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$new(data)"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"data dataframe containing input data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"new instance NumericBlockFinder class. Find upper-left cell first 6x6 block numeric values method identifies upper-left cell first 6x6 block numeric values dataframe.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$find_upper_left_cell()"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"list containing row column indices upper-left cell. Find lower-right cell block contiguous non-NA values method identifies lower-right cell block contiguous non-NA values starting given upper-left cell dataframe.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$find_lower_right_cell()"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"list containing row column indices lower-right cell.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"objects class cloneable method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"","code":"NumericBlockFinder$clone(deep = FALSE)"},{"path":"https://csbg.github.io/SplineOmics/reference/NumericBlockFinder.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"NumericBlockFinder: A class for finding numeric blocks in data β NumericBlockFinder","text":"deep Whether make deep clone.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Package Name: SplineOmics β SplineOmics-package","title":"Package Name: SplineOmics β SplineOmics-package","text":"R package SplineOmics finds significant features (hits) time-series -omics data using splines limma hypothesis testing. clusters hits based spline shape showing results summary HTML reports. detailed documentation, vignettes, examples, please visit [SplineOmics GitHub page](https://github.com/csbg/SplineOmics.git).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"key-functions-and-classes","dir":"Reference","previous_headings":"","what":"Key Functions and Classes","title":"Package Name: SplineOmics β SplineOmics-package","text":"- extract_data: Extracts data matrix Excel file. - create_splineomics: Creates SplineOmics object, contains arguments used several package functions. - explore_data: Performs exploratory data analysis data, outputs HTML report containg various plots, density plots correlation heatmaps. - screen_limma_hyperparams: Allows specify lists different hyperparameters test, degree freedom 2, 3, 4, adj.p-val thresholds, 0.1 0.05, tests specified different values limma spline hyperparameters semi-combinatorial way. - update_splineomics: Allows change values SplineOmics object, example observing outliers removed data (update data parameter). - run_limma_splines: Central function script, called screen_limma_hyperparams function can called get limma spline analysis results (p-values features (e.g. proteins)) hyperparameters, selected finally. - create_limma_report: Creates HTML report showing run_limma_splines results - cluster_hits: Clusters splines hits (significant features) based shape shows results plots HTML report. - download_enrichr_databases: Allows download Enrichr databases runnin clusterProfiler run_gsea function . - run_gsea: Runs clusterProfiler clustered hits using Enrichr databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package Options","title":"Package Name: SplineOmics β SplineOmics-package","text":"None","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"dependencies","dir":"Reference","previous_headings":"","what":"Dependencies","title":"Package Name: SplineOmics β SplineOmics-package","text":"- **ComplexHeatmap**: creating complex heatmaps advanced features. - **base64enc**: encoding/decoding base64. - **dendextend**: extending `dendrogram` objects R, allowing easier manipulation dendrograms. - **dplyr**: data manipulation. - **ggplot2**: creating elegant data visualizations using grammar graphics. - **ggrepel**: better label placement ggplot2. - ****: constructing paths projectβs files. - **limma**: linear models microarray data. - **openxlsx**: reading, writing, editing xlsx files. - **patchwork**: combining multiple ggplot objects single plot. - **pheatmap**: creating pretty heatmaps. - **progress**: adding progress bars loops apply functions. - **purrr**: functional programming tools. - **rlang**: tools work core language features R Rβs base types. - **scales**: scale functions visualization. - **tibble**: creating tidy data frames easy work . - **tidyr**: tidying data. - **zip**: combining files zip file. Optional dependencies dependencies necessary functions: - **edgeR**: preprocessing RNA-seq data run_limma_splines() fun. - **clusterProfiler**: run_gsea() function (gene set enrichment). - **rstudioapi**: open_tutorial() open_template() functions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"authors","dir":"Reference","previous_headings":"","what":"Authors","title":"Package Name: SplineOmics β SplineOmics-package","text":"- [Thomas-Rauter](https://github.com/Thomas-Rauter) - Wrote package developed approach VSchaepertoens guidance nfortelny skafdasschaf. - [nfortelny](https://github.com/nfortelny) - Principal Investigator, provided guidance support. - [skafdasschaf](https://github.com/skafdasschaf) - Helped review code provided improvement suggestions. - [VSchaepertoens](https://github.com/VSchaepertoens) - Developed internal plotting function contributed exploratory data analysis overall approach.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"maintainer","dir":"Reference","previous_headings":"","what":"Maintainer","title":"Package Name: SplineOmics β SplineOmics-package","text":"- Name: Thomas Rauter - Email: thomas.rauter@plus.ac.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"license","dir":"Reference","previous_headings":"","what":"License","title":"Package Name: SplineOmics β SplineOmics-package","text":"- License: MIT","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"useful-urls","dir":"Reference","previous_headings":"","what":"Useful URLs","title":"Package Name: SplineOmics β SplineOmics-package","text":"- [GitHub repo package](https://github.com/csbg/SplineOmics.git)","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"additional-information","dir":"Reference","previous_headings":"","what":"Additional Information","title":"Package Name: SplineOmics β SplineOmics-package","text":"None","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/SplineOmics-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Package Name: SplineOmics β SplineOmics-package","text":"Maintainer: Thomas Rauter thomas.rauter@plus.ac.(ORCID)","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"internal function adds dashed vertical lines specified treatment timepoints plot, along text labels display corresponding x-axis values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"","code":"add_dashed_lines( p, treatment_timepoints, treatment_labels, y_pos = 1, horizontal_labels = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"p ggplot object. plot dashed lines labels added. treatment_timepoints numeric vector timepoints dashed lines drawn. treatment_labels character vector labels corresponding treatment timepoint. labels used coloring lines, x-axis coordinates displayed labels. y_pos numeric value specifying y-axis position text labels placed. horizontal_labels Boolean flag indicating whether vertical label (default) horizontal label.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_dashed_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add dashed lines for treatment timepoints to a plot β add_dashed_lines","text":"ggplot object added dashed lines labels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Feature Names to Data β add_feature_names","title":"Add Feature Names to Data β add_feature_names","text":"function assigns feature names rows dataframe based specified column another dataframe. column specified, assigns sequential numbers feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Feature Names to Data β add_feature_names","text":"","code":"add_feature_names(data, clean_data, feature_name_columns)"},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Feature Names to Data β add_feature_names","text":"data dataframe containing original data feature names. clean_data dataframe feature names added. feature_name_columns string specifying name feature columns `data`. `NA`, sequential numbers used feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Feature Names to Data β add_feature_names","text":"`clean_data` dataframe updated row names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_feature_names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Feature Names to Data β add_feature_names","text":"function performs following operations: - Extracts feature names specified column `data`, ignoring `NA` values. - Ensures feature names unique match number rows `clean_data`. - Assigns feature names rows `clean_data`. - `feature_name_column` `NA`, assigns sequential numbers (1, 2, 3, etc.) feature names issues message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Plot to HTML Content β add_plot_to_html","title":"Add Plot to HTML Content β add_plot_to_html","text":"function converts plot base64 image adds HTML content.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Plot to HTML Content β add_plot_to_html","text":"","code":"add_plot_to_html(html_content, plot_element, plots_size, section_index)"},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Plot to HTML Content β add_plot_to_html","text":"html_content current HTML content character string. plot_element plot element converted base64. plots_size integer specifying height plot. section_index integer specifying section index.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/add_plot_to_html.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Plot to HTML Content β add_plot_to_html","text":"updated HTML content character string.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":null,"dir":"Reference","previous_headings":"","what":"Prompt the user with a yes/no question β ask_user","title":"Prompt the user with a yes/no question β ask_user","text":"function prompts user yes/question. user answers \"yes\" (case insensitive), code proceeds. user answers \"\" anything else, code stops.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prompt the user with a yes/no question β ask_user","text":"","code":"ask_user(question)"},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prompt the user with a yes/no question β ask_user","text":"question string question ask user.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ask_user.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prompt the user with a yes/no question β ask_user","text":"None.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Between Level Analysis β between_level","title":"Between Level Analysis β between_level","text":"Performs -level analysis using LIMMA compare specified levels within condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Between Level Analysis β between_level","text":"","code":"between_level( data, rna_seq_data, meta, design, dream_params, spline_params, condition, compared_levels, padjust_method, feature_names )"},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Between Level Analysis β between_level","text":"data matrix data values. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta dataframe containing metadata, including 'Time' column. design design formula matrix LIMMA analysis. dream_params named list NULL. NULL, must least contain named element 'random_effects', must contain string formula random effects mixed models dream. Additionally, can contain named elements dof, must int bigger 1, degree freedom dream topTable, named element KenwardRoger, must bool, specifying whether use method . spline_params list spline parameters analysis. condition character string specifying condition. compared_levels vector levels within condition compare. padjust_method character string specifying p-adjustment method. feature_names non-empty character vector feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/between_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Between Level Analysis β between_level","text":"list containing top tables factor factor-time contrast.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Bind Data with Annotation β bind_data_with_annotation","title":"Bind Data with Annotation β bind_data_with_annotation","text":"function converts matrix dataframe, adds row names first column, binds annotation data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bind Data with Annotation β bind_data_with_annotation","text":"","code":"bind_data_with_annotation(data, annotation = NULL)"},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bind Data with Annotation β bind_data_with_annotation","text":"data matrix containing numeric data. annotation dataframe containing annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/bind_data_with_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bind Data with Annotation β bind_data_with_annotation","text":"dataframe `data` `annotation` combined, row names `data` first column named `feature_names`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Cluster Hits Report β build_cluster_hits_report","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"Generates HTML report clustered hits, including plots spline parameter details, table contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"","code":"build_cluster_hits_report( header_section, plots, limma_result_2_and_3_plots, plots_sizes, level_headers_info, spline_params, adj_pthresholds, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction_condition_time, mode, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"header_section character string containing HTML header section. plots list ggplot2 plot objects. limma_result_2_and_3_plots List containing list lists plots pairwise comparisons condition terms average spline diff interaction condition time, another list lists respective names plot stored. plots_sizes list integers specifying size plot. level_headers_info list header information level. spline_params list spline parameters. adj_pthresholds Float vector values level adj.p.tresh adj_pthresh_avrg_diff_conditions Float adj_pthresh_interaction_condition_time Float mode character string specifying mode ('isolated' 'integrated'). report_info named list containg report info fields. used email hotkey functionality. output_file_path character string specifying path save HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_cluster_hits_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Cluster Hits Report β build_cluster_hits_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build GSEA Report β build_create_gsea_report","title":"Build GSEA Report β build_create_gsea_report","text":"Generates HTML report Gene Set Enrichment Analysis (GSEA) based provided plot data, header information, content. report includes sections level clustered hits, along table contents various plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build GSEA Report β build_create_gsea_report","text":"","code":"build_create_gsea_report( header_section, plots, plots_sizes, level_headers_info, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build GSEA Report β build_create_gsea_report","text":"header_section string containing HTML content header section report. plots list plots included report. plots_sizes list sizes plots. level_headers_info list containing header information level clustered hits. report_info named list containg report info fields. used email hotkey functionality. output_file_path string specifying file path report saved.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build GSEA Report β build_create_gsea_report","text":"None. function generates writes HTML report specified output file path.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_gsea_report.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Build GSEA Report β build_create_gsea_report","text":"function first initializes HTML content provided header section placeholder table contents (TOC). iterates plots, generating sections level clustered hits processing individual plots. TOC inserted HTML content, finalized written specified output file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Cluster Hits Report β build_create_limma_report","title":"Build Cluster Hits Report β build_create_limma_report","text":"Generates HTML report clustered hits, including plots spline parameter details, table contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Cluster Hits Report β build_create_limma_report","text":"","code":"build_create_limma_report( header_section, plots, plots_sizes, level_headers_info, report_info, output_file_path = here::here() )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Cluster Hits Report β build_create_limma_report","text":"header_section character string containing HTML header section. plots list ggplot2 plot objects. plots_sizes list integers specifying size plot. level_headers_info list header information level. report_info named list containg report info fields. used email hotkey functionality. output_file_path character string specifying path save HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_create_limma_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Cluster Hits Report β build_create_limma_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Explore Data Report β build_explore_data_report","title":"Build Explore Data Report β build_explore_data_report","text":"function generates HTML report containing header section, table contents, series plots. plot included report specified sizes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Explore Data Report β build_explore_data_report","text":"","code":"build_explore_data_report( header_section, plots, plots_sizes, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Explore Data Report β build_explore_data_report","text":"header_section string containing HTML content header section report. plots list ggplot objects representing plots included report. plots_sizes list sizes corresponding plot, defining dimensions used rendering plots. report_info named list containg report info fields. used email hotkey functionality. output_file_path string specifying file path HTML report saved.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_explore_data_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Explore Data Report β build_explore_data_report","text":"None. function writes HTML content specified file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"Constructs HTML report hyperparameter screening embedding plots respective sizes provided header section.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"","code":"build_hyperparams_screen_report( header_section, plots, plots_sizes, report_info, output_file_path )"},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"header_section character string containing HTML header section. plots list ggplot2 plot objects. plots_sizes list integers specifying number rows plot. report_info named list containg report info fields. used email hotkey functionality. output_file_path character string specifying path save HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/build_hyperparams_screen_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Hyperparameters Screening Report β build_hyperparams_screen_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate average CV across unique time points β calc_cv","title":"Calculate average CV across unique time points β calc_cv","text":"function calculates coefficient variation (CV) unique time point based provided time values response values. returns average CV across time points. CV calculated one valid (non-NA) values given time point mean values non-zero.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate average CV across unique time points β calc_cv","text":"","code":"calc_cv(time_values, response_values)"},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate average CV across unique time points β calc_cv","text":"time_values numeric vector containing time points. Time points may repeat across replicates. response_values numeric vector response values corresponding time points.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/calc_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate average CV across unique time points β calc_cv","text":"average coefficient variation (CV) across time points. Returns NA CVs NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"function checks elements within list top tables contain element names match specified -level pattern.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"","code":"check_between_level_pattern(top_tables)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"top_tables list element list containing named elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"list two elements: between_levels logical value indicating whether element names match -level pattern. index_with_pattern index first element `top_tables` names match -level pattern, NA match found.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_between_level_pattern.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for Between-Level Patterns in Top Tables β check_between_level_pattern","text":"function iterates element `top_tables`. element list, checks names within inner list match pattern `\".+_vs_.+\"`. match found, function sets `between_levels` TRUE records index matching element. search stops first match.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"function checks given dataframe `clustered_genes` contains required columns `gene` `cluster`. `gene` column must contain character strings length 1, `cluster` column must contain integers. condition met, function stops script produces informative error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"","code":"check_clustered_hits(levels_clustered_hits)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"levels_clustered_hits list dataframes checked required format.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_clustered_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Clustered Genes Dataframe for Required Conditions β check_clustered_hits","text":"function return value. stops error message conditions met.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Valid Databases Dataframe β check_databases","title":"Check Valid Databases Dataframe β check_databases","text":"function checks dataframe exactly three columns named DB, Geneset, Gene, columns must type character.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Valid Databases Dataframe β check_databases","text":"","code":"check_databases(databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Valid Databases Dataframe β check_databases","text":"databases dataframe check.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_databases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Valid Databases Dataframe β check_databases","text":"None. function stops execution provides error message dataframe valid.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Valid Gene IDs β check_genes","title":"Check Valid Gene IDs β check_genes","text":"function checks whether character vector `genes` contains valid gene IDs. gene ID must consist solely alphabetic letters numbers.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Valid Gene IDs β check_genes","text":"","code":"check_genes(genes, max_index_overall = NA)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Valid Gene IDs β check_genes","text":"genes character vector containing gene IDs. max_index_overall integer, specifying highest index features across levels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Valid Gene IDs β check_genes","text":"None. function stops execution provides error message vector meet criteria, including first offending element index.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for NULL Elements in Arguments β check_null_elements","title":"Check for NULL Elements in Arguments β check_null_elements","text":"function checks elements provided list arguments `NULL`. `NULL` elements found, stops execution returns informative error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for NULL Elements in Arguments β check_null_elements","text":"","code":"check_null_elements(args)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for NULL Elements in Arguments β check_null_elements","text":"args list arguments check `NULL` elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_null_elements.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for NULL Elements in Arguments β check_null_elements","text":"function return value. stops execution `NULL` elements found input list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Params List for Required Conditions β check_params","title":"Check Params List for Required Conditions β check_params","text":"function checks given list `params` contains allowed named elements. elements present, , must named exactly specified must contain correct data types: float, character, int, int, float. condition met, function stops script produces informative error message. `params` can also `NA`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Params List for Required Conditions β check_params","text":"","code":"check_params(params)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Params List for Required Conditions β check_params","text":"params list checked required conditions, `NA`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Params List for Required Conditions β check_params","text":"function return value. stops error message conditions met.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"function checks given object contains required named elements specified function type. element missing, stops script provides informative error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"","code":"check_splineomics_elements(splineomics, func_type)"},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"splineomics object checked. func_type string specifying function type. can one \"cluster_hits\", \"create_limma_report\", \"run_limma_splines\", \"explore_data\"","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/check_splineomics_elements.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for Required Elements in the SplineOmics Object β check_splineomics_elements","text":"None. Stops execution required element missing.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean the Gene Symbols β clean_gene_symbols","title":"Clean the Gene Symbols β clean_gene_symbols","text":"function preprocesses vector gene names cleaning formatting . removes non-alphanumeric characters first block alphanumeric characters converts remaining characters uppercase.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean the Gene Symbols β clean_gene_symbols","text":"","code":"clean_gene_symbols(genes)"},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean the Gene Symbols β clean_gene_symbols","text":"genes character vector containing gene names cleaned.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/clean_gene_symbols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean the Gene Symbols β clean_gene_symbols","text":"character vector cleaned gene symbols (names) length input. cleaned names uppercase, invalid empty gene names replaced NA.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster hits (significant features) based on the spline shape β cluster_hits","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"Performs clustering hits top tables generated differential expression analysis. function filters hits based adjusted p-value thresholds, extracts spline coefficients significant features, normalizes coefficients, applies hierarchical clustering. results, including clustering assignments normalized spline curves, saved specified directory compiled HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"","code":"cluster_hits( splineomics, clusters, adj_pthresholds = c(0.05), adj_pthresh_avrg_diff_conditions = 0, adj_pthresh_interaction_condition_time = 0, genes = NULL, plot_info = list(y_axis_label = \"Value\", time_unit = \"min\", treatment_labels = NA, treatment_timepoints = NA), plot_options = list(cluster_heatmap_columns = FALSE, meta_replicate_column = NULL), raw_data = NULL, report_dir = here::here(), report = TRUE )"},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"splineomics S3 object class `SplineOmics` contains necessary data parameters analysis, including: data: original expression dataset used differential expression analysis. meta: dataframe containing metadata corresponding data, must include 'Time' column columns specified conditions. design: character length 1 representing limma design formula. condition: Character length 1 specifying column name meta used define groups analysis. spline_params: list spline parameters analysis. meta_batch_column: character string specifying column name metadata used batch effect removal. meta_batch2_column: character string specifying second column name metadata used batch effect removal. limma_splines_result: list data frames, representing top table differential expression analysis, containing least 'adj.P.Val' expression data columns. clusters Character integer vector specifying number clusters adj_pthresholds Numeric vector p-value thresholds filtering hits top table. adj_pthresh_avrg_diff_conditions p-value threshold results average difference condition limma result. Per default 0 ( turned ). adj_pthresh_interaction_condition_time p-value threshold results interaction condition time limma result. Per default 0 (turned ). genes character vector containing gene names features analyzed. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). plot_options List specific fields (cluster_heatmap_columns = Bool) allow customization plotting behavior. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed. report_dir Character string specifying directory path HTML report output files saved. report Boolean TRUE FALSE value specifing report generated.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/cluster_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster hits (significant features) based on the spline shape β cluster_hits","text":"list element corresponds group factor contains clustering results, including `clustered_hits` data frame, hierarchical clustering object `hc`, `curve_values` data frame normalized spline curves, `top_table` cluster assignments.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_create_gsea_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","title":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","text":"Validates inputs generating GSEA report, including clustered hits, genes, databases, parameters, plot titles, background genes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_create_gsea_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","text":"","code":"control_inputs_create_gsea_report( levels_clustered_hits, databases, params, plot_titles, background )"},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_create_gsea_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Inputs for GSEA Report β control_inputs_create_gsea_report","text":"levels_clustered_hits list containing clustered hits various levels. databases list databases used GSEA analysis. params list parameters GSEA analysis. plot_titles character vector titles plots, length matching `levels_clustered_hits`. background character vector background genes NULL.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Control Inputs for Extracting Data β control_inputs_extract_data","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"function checks validity input data feature name column. ensures input data dataframe, feature name column specified correctly, contains valid data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"","code":"control_inputs_extract_data(data, feature_name_columns)"},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"data dataframe containing input data. feature_name_columns character vector specifying names feature name columns. columns must present dataframe data. `NA`, column checked.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/control_inputs_extract_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Control Inputs for Extracting Data β control_inputs_extract_data","text":"function performs following checks: - Ensures input data dataframe. - Checks feature name column single string exists data. - Ensures specified feature name column contain `NA` values. - Checks input dataframe empty. checks fail, function stops error message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"function creates ZIP file containing directories level gene lists. directory contains text files cluster. ZIP file encoded base64 easy download.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"","code":"create_enrichr_zip(enrichr_format)"},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"enrichr_format list formatted gene lists background gene list, typically output `prepare_gene_lists_for_enrichr`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"base64-encoded string representing ZIP file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_enrichr_zip.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a ZIP File for Enrichr Gene Lists β create_enrichr_zip","text":"function creates temporary directory store files. level `enrichr_format$gene_lists`, creates directory named level. Within level directory, creates text file cluster, containing genes cluster. directories files added ZIP file, encoded base64.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"function conducts Gene Set Enrichment Analysis (GSEA) using either clusterProfiler package. Afterwards, plots results. allows customization enrichment parameters, selection databases, optionally specifying custom plot title background gene list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"","code":"create_gsea_report_level( clustered_genes, databases, params = NA, plot_title = \"\", universe = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"clustered_genes list dataframes two columns: first column contains standard gene symbol, second column contains integer specifying cluster. databases dataframe containing data downloaded Enrichr databases params list specifying clusterProfiler parameters enrichment analysis. plot_title optional string specifying title plot. provided, default title based analysis used. universe optional list standard gene symbols used background enrichment analysis instead background chosen `enricher`. default empty list, implies use default background set enrichment tool.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_gsea_report_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Gene Set Enrichment Analysis and plot it. β create_gsea_report_level","text":"object containing results Gene Set Enrichment Analysis, including plots generated analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"Generates HTML report based results limma analysis splines. report includes various plots sections summarizing analysis results time effects, average differences conditions, interaction effects condition time.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"","code":"create_limma_report(splineomics, adj_pthresh = 0.05, report_dir = here::here())"},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"splineomics S3 object class `SplineOmics` contains necessary data parameters analysis, including: limma_splines_result: list containing top tables differential expression analysis three different limma results. meta: data frame sample metadata. Must contain column \"Time\". condition: character string specifying column name metadata (meta) defines groups analysis. column contains levels \"exponential\" \"stationary\" phases, \"drug\" \"no_drug\" treatments. annotation: data frame containing feature information, gene protein names, associated expression data. report_info: list containing metadata analysis reporting purposes. adj_pthresh numeric value specifying adjusted p-value threshold significance. Default 0.05. Must > 0 < 1. report_dir string specifying directory report saved. Default current working directory.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_limma_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shows the limma results with p-value histograms and volcano plots in an HTML report β create_limma_report","text":"list plots included generated HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a p-value histogram from a limma top_table β create_p_value_histogram","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"function generates histogram unadjusted p-values limma top_table.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"","code":"create_p_value_histogram(top_table, title = \"P-Value Histogram\")"},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"top_table data frame containing limma top_table column named `P.Value` unadjusted p-values. title character string title histogram.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_p_value_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a p-value histogram from a limma top_table β create_p_value_histogram","text":"ggplot2 object representing histogram unadjusted p-values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":null,"dir":"Reference","previous_headings":"","what":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"Creates progress bar tracking progress iterable task.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"","code":"create_progress_bar(iterable, message = \"Processing\")"},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"iterable iterable object (e.g., list vector) whose length determines total number steps. message message display progress bar (default \"Processing\").","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_progress_bar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. Create Progress Bar β create_progress_bar","text":"progress bar object 'progress' package.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Spline Parameters β create_spline_params","title":"Create Spline Parameters β create_spline_params","text":"Generates spline parameters based configuration, metadata, condition, mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Spline Parameters β create_spline_params","text":"","code":"create_spline_params(spline_test_configs, index, meta, condition, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Spline Parameters β create_spline_params","text":"spline_test_configs configuration object spline tests. index Index spline configuration process. meta dataframe containing metadata. condition character string specifying condition. mode character string specifying mode.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_spline_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Spline Parameters β create_spline_params","text":"list processed spline parameters.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"Creates SplineOmics object containing variables commonly used across multiple functions package.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"","code":"create_splineomics( data, meta, condition, rna_seq_data = NULL, annotation = NULL, report_info = NULL, meta_batch_column = NULL, meta_batch2_column = NULL, feature_name_columns = NULL, design = NULL, dream_params = NULL, mode = NULL, spline_params = NULL, padjust_method = \"BH\" )"},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"data actual omics data. case rna_seq_data argument used, still provide argument. case, input data matrix (example $E part voom object). Assign feature names row headers (otherwise, just numbers feature names). meta Metadata associated omics data. condition condition variable. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. argument subjected input control. Rather, regard relies input control `limma::lmfit` function. annotation dataframe feature descriptions data (optional). report_info list containing report information omics data type, data description, data collection date, analyst name, contact info, project name (optional). meta_batch_column Column meta batch information (optional). meta_batch2_column Column secondary meta batch information (optional). feature_name_columns Character vector containing column names annotation info describe features. argument used specify HTML report exactly feature names displayed individual spline plot created. Use vector used create row headers data matrix! design design matrix similar object (optional). mode design formula, must specify either 'isolated' 'integrated'. Isolated means limma determines results level using data level. Integrated means limma determines results levels using full dataset (levels). spline_params Parameters spline functions (optional). Must contain named elements spline_type, must contain either string \"n\" natural cubic splines, \"b\", B-splines, named element degree case B-splines, must contain integer, named element dof, specifying degree freedom, containing integer required natural B-splines. padjust_method Method p-value adjustment, one \"none\", \"BH\", \"\", \"holm\", \"bonferroni\", \"hochberg\", \"hommel\". Defaults \"BH\" (Benjamini-Hochberg).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_splineomics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a SplineOmics object, which holds common variables used by several functions of the package β create_splineomics","text":"SplineOmics object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_toc.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Table of Contents β create_toc","title":"Create Table of Contents β create_toc","text":"Creates HTML content Table Contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_toc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Table of Contents β create_toc","text":"","code":"create_toc()"},{"path":"https://csbg.github.io/SplineOmics/reference/create_toc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Table of Contents β create_toc","text":"string containing HTML Table Contents.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Volcano Plot β create_volcano_plot","title":"Create a Volcano Plot β create_volcano_plot","text":"function creates volcano plot limma top table, plotting log fold changes negative log10 adjusted p-values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Volcano Plot β create_volcano_plot","text":"","code":"create_volcano_plot(top_table, adj_pthresh, compared_levels)"},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Volcano Plot β create_volcano_plot","text":"top_table data frame limma containing 'logFC' 'adj.P.Val' columns. adj_pthresh numeric value adjusted p-value threshold. compared_levels character vector length 2 specifying compared levels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/create_volcano_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Volcano Plot β create_volcano_plot","text":"ggplot object representing volcano plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Database File to TERM2GENE List β dbs_to_term2genes","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"Reads specified .tsv file containing information databases, gene sets, genes. file three columns: 'DB' database names, Geneset' gene set identifiers, 'Gene' gene names. function organizes information nested list. top-level element corresponds unique database, within , gene sets map lists associated genes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"","code":"dbs_to_term2genes(databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"databases dataframe, containing three columns DB, Geneset, gene. dataframe contains databases downloaded Enrichr SplineOmics package function: download_enrichr_databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/dbs_to_term2genes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Database File to TERM2GENE List β dbs_to_term2genes","text":"nested list first level names corresponds database names ('DB'), second level gene sets ('Geneset'), innermost lists contain gene names ('Gene') associated gene set.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/define_html_styles.html","id":null,"dir":"Reference","previous_headings":"","what":"Define HTML Styles β define_html_styles","title":"Define HTML Styles β define_html_styles","text":"Defines CSS styles section headers Table Contents (TOC) entries used GSEA report generation.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/define_html_styles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define HTML Styles β define_html_styles","text":"","code":"define_html_styles()"},{"path":"https://csbg.github.io/SplineOmics/reference/define_html_styles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define HTML Styles β define_html_styles","text":"list containing styles section headers TOC entries.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Design Matrix for Splines β design2design_matrix","title":"Create Design Matrix for Splines β design2design_matrix","text":"function generates design matrix using spline parameters metadata. accommodates B-splines natural cubic splines based provided spline type parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Design Matrix for Splines β design2design_matrix","text":"","code":"design2design_matrix(meta, spline_params, level_index, design)"},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Design Matrix for Splines β design2design_matrix","text":"meta dataframe containing metadata, including time column. spline_params list containing spline parameters. list can include `dof` (degrees freedom), `knots`, `bknots` (boundary knots), `spline_type`, `degree`. level_index integer representing current level index design matrix generated. design character string representing design formula used generating model matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/design2design_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Design Matrix for Splines β design2design_matrix","text":"design matrix constructed using specified spline parameters design formula.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads specified Enrichr databases from the web β download_enrichr_databases","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"function downloads gene sets specified Enrichr databases saves specified output directory .tsv file. file named timestamp ensure uniqueness.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"","code":"download_enrichr_databases( gene_set_lib, output_dir = here::here(), filename = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"gene_set_lib character vector database names download Enrichr. output_dir character string specifying output directory .tsv file saved. Defaults current working directory. filename Name output file (file extension. Due commas present terms, .tsv recommendet). ommited, file named all_databases_timestamp.tsv.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/download_enrichr_databases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads specified Enrichr databases from the web β download_enrichr_databases","text":"function return value saves .tsv file specified directory containing gene sets specified Enrichr databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":null,"dir":"Reference","previous_headings":"","what":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"function takes dataframe input returns base64 encoded CSV object. encoded object can embedded HTML document directly, button download file without pointing local file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"","code":"encode_df_to_base64(df, report_type = NA)"},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"df dataframe encoded. report_type (Optional) string specifying report generation function called. Generates different Excel sheet names based report_type.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/encode_df_to_base64.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encode DataFrame to Base64 for HTML Embedding β encode_df_to_base64","text":"character string containing base64 encoded CSV data.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Enrichr Gene Sets β enrichr_get_genesets","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"function downloads gene sets specified Enrichr databases. returns list element list corresponding database, element containing vector human gene symbols gene set.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"","code":"enrichr_get_genesets(databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"databases character vector database names download Enrichr.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/enrichr_get_genesets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Enrichr Gene Sets β enrichr_get_genesets","text":"named list gene sets specified Enrichr databases. database represented list, gene set names list names vectors human gene symbols list elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ensure_clusterProfiler.html","id":null,"dir":"Reference","previous_headings":"","what":"Ensure 'clusterProfiler' is installed and loaded β ensure_clusterProfiler","title":"Ensure 'clusterProfiler' is installed and loaded β ensure_clusterProfiler","text":"function checks 'clusterProfiler' package installed. , prompts user choose whether install automatically, install manually, cancel operation. installed, package loaded use.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/ensure_clusterProfiler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ensure 'clusterProfiler' is installed and loaded β ensure_clusterProfiler","text":"","code":"ensure_clusterProfiler()"},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates exploratory plots of the data matrix such as PCA β explore_data","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"function takes data matrix, checks validity, generates list exploratory plots including density plots, boxplots, PCA plots, MDS plots, variance explained plots, violin plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"","code":"explore_data(splineomics, report_dir = here::here(), report = TRUE)"},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"splineomics SplineOmics object, containing data, meta, condition, report_info, meta_batch_column, meta_batch2_column; report_dir non-empty string specifying report directory. report Boolean TRUE FALSE value, specifying report generated . report generated per default, plots plot objects inside R desired, argument can set FALSE.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/explore_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates exploratory plots of the data matrix such as PCA β explore_data","text":"list ggplot objects representing various exploratory plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"function takes dataframe identifies rectangular quadratic area containing numeric data, starting first occurrence 6x6 block numeric values. extracts area matrix, ensuring row contains numeric values. Rows NA values removed resulting matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"","code":"extract_data(data, feature_name_columns = NA, user_prompt = TRUE)"},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"data dataframe loaded tabular file, potentially containing rectangular quadratic area numeric data amidst values. feature_name_columns (Optional) character vector, specifying columns dataframe data, used construct feature names. ommited, feature names just numbers (stored characters) starting 1 (1, 2, 3, etc.) user_prompt Boolean specifying whether user prompt correct format input data shown.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/extract_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optional convenience function for extracting a numeric matrix from dataframe β extract_data","text":"numeric matrix row headers appropriate column names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten Spline Configurations β flatten_spline_configs","title":"Flatten Spline Configurations β flatten_spline_configs","text":"Flattens formats spline configurations list formatted strings.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten Spline Configurations β flatten_spline_configs","text":"","code":"flatten_spline_configs(spline_configs)"},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten Spline Configurations β flatten_spline_configs","text":"spline_configs list spline configuration objects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/flatten_spline_configs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flatten Spline Configurations β flatten_spline_configs","text":"list formatted strings representing spline configuration.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Format text β format_text","title":"Format text β format_text","text":"function takes character vector `text` splits individual characters. iterates characters builds lines exceeding specified character limit (default 70). Newlines inserted lines using `
` tag, suitable HTML display.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format text β format_text","text":"","code":"format_text(text)"},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format text β format_text","text":"text character vector formatted.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/format_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format text β format_text","text":"character vector formatted text containing line breaks.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Composite Spline Plots β gen_composite_spline_plots","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"Creates composite spline plots significant non-significant features across multiple levels within condition. One half one condition comparison HTML (composite spline plots one 'condition' inside one condition comparison)","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"","code":"gen_composite_spline_plots( internal_combos, datas, metas, spline_test_configs, time_unit_label )"},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"internal_combos list containing combinations top tables. datas list matrices. metas list metadata corresponding data matrices. spline_test_configs configuration object spline tests. time_unit_label character string specifying time unit label plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_composite_spline_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Composite Spline Plots β gen_composite_spline_plots","text":"list containing composite spline plots lengths.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Hit Comparison Plots β gen_hitcomp_plots","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"Generates Venn heatmap barplot given combination pair top tables.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"","code":"gen_hitcomp_plots(combo_pair)"},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"combo_pair list containing two combinations top tables.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/gen_hitcomp_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Hit Comparison Plots β gen_hitcomp_plots","text":"list containing Venn heatmap plot, number hits divided 16, barplot, length indicator barplot.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/generate_and_write_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate and Write HTML Report β generate_and_write_html","title":"Generate and Write HTML Report β generate_and_write_html","text":"function generates HTML report inserting table contents, embedding necessary JavaScript files, writing final HTML content specified output file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_and_write_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate and Write HTML Report β generate_and_write_html","text":"","code":"generate_and_write_html(toc, html_content, report_info, output_file_path)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_and_write_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate and Write HTML Report β generate_and_write_html","text":"toc string containing table contents HTML format. html_content string containing main HTML content placeholder table contents. report_info list containing report information `contact_info` `analyst_name`. output_file_path string specifying path final HTML file written.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"Creates p-value histograms volcano plots condition average difference conditions. function used internally `create_limma_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"","code":"generate_avrg_diff_plots(avrg_diff_conditions, adj_pthresh)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"avrg_diff_conditions list top tables LIMMA analysis representing average difference conditions. adj_pthresh numeric value specifying adjusted p-value threshold significance.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_avrg_diff_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Plots for Average Difference Conditions β generate_avrg_diff_plots","text":"list containing plots sizes, well section header information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate exploratory plots β generate_explore_plots","title":"Generate exploratory plots β generate_explore_plots","text":"function generates various exploratory plots including density plots, box plots, violin plots, PCA plots, correlation heatmaps based provided data metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate exploratory plots β generate_explore_plots","text":"","code":"generate_explore_plots(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate exploratory plots β generate_explore_plots","text":"data data frame matrix containing data plotted. meta data frame containing metadata associated data. condition string specifying column metadata contains condition grouping variable.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_explore_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate exploratory plots β generate_explore_plots","text":"list containing two elements: plots list ggplot objects representing generated plots. plots_sizes vector numeric values indicating sizes corresponding plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"Creates p-value histograms interaction condition interaction condition time. function used internally `create_limma_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"","code":"generate_interaction_plots(interaction_condition_time, adj_pthresh)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"interaction_condition_time list top tables LIMMA analysis representing interaction effects condition time. adj_pthresh numeric value specifying adjusted p-value threshold significance.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_interaction_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Plots for Interaction of Condition and Time β generate_interaction_plots","text":"list containing plots sizes, well section header information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":null,"dir":"Reference","previous_headings":"","what":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"Generates HTML report provided plots, spline parameters, report information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"","code":"generate_report_html( plots, plots_sizes, report_info, limma_result_2_and_3_plots = NULL, data = NULL, meta = NA, topTables = NA, enrichr_format = NA, level_headers_info = NA, spline_params = NA, adj_pthresholds = NA, adj_pthresh_avrg_diff_conditions = NA, adj_pthresh_interaction_condition_time = NA, report_type = \"explore_data\", feature_name_columns = NA, mode = NA, filename = \"report\", timestamp = format(Sys.time(), \"%d_%m_%Y-%H_%M_%S\"), report_dir = here::here() )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"plots list ggplot2 plot objects. plots_sizes list integers specifying size plot. report_info named list containing report information. limma_result_2_and_3_plots List containing list lists plots pairwise comparisons condition terms average spline diff interaction condition time, another list lists respective names plot stored. data dataframe list dataframes, containing data directly embedded HTML report downloading. meta dataframe, containing metadata directly embedded HTML report downloading. topTables List limma topTables enrichr_format List, containing two lists: gene list list background genes. level_headers_info list header information level. spline_params list spline parameters, dof type. adj_pthresholds Numeric vector values adj.p.tresholds level. adj_pthresh_avrg_diff_conditions Float, cluster_hits() adj_pthresh_interaction_condition_time Float, cluster_hits() report_type character string specifying report type ('screen_limma_hyperparams' 'cluster_hits'). feature_name_columns Character vector column names annotation information, columns containing gene names. column names used put info HTML reports descriptions individual spline plots created. descriptions can made several column values, specific columns stated HTML report top (e.g gene_uniprotID). mode character string specifying mode ('isolated' 'integrated'). filename character string specifying filename report. timestamp timestamp include report filename. report_dir character string specifying report directory.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_report_html.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"utils scripts contains shared functions that are used by at least two package functions of the SplineOmics package. The level separation is only valid internally in this script, and has no connection to the script level of the respective exported functions scripts. Generate Report HTML β generate_report_html","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Reports β generate_reports","title":"Generate Reports β generate_reports","text":"Builds HTML reports pairwise hyperparameter combination comparisons.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Reports β generate_reports","text":"","code":"generate_reports(combo_pair_plots, report_info, report_dir, timestamp)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Reports β generate_reports","text":"combo_pair_plots list plots pair combinations. report_info object containing report information. report_dir non-empty string specifying report directory. timestamp timestamp include reports.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Reports β generate_reports","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Reports Metadata β generate_reports_meta","title":"Generate Reports Metadata β generate_reports_meta","text":"Generates metadata table LIMMA hyperparameter screen reports saves HTML file custom styling.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Reports Metadata β generate_reports_meta","text":"","code":"generate_reports_meta( datas_descr, designs, modes, spline_test_configs, report_dir, timestamp )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Reports Metadata β generate_reports_meta","text":"datas_descr description object data. designs list design matrices. modes character vector containing 'isolated' 'integrated'. spline_test_configs configuration object spline tests. report_dir non-empty string specifying report directory. timestamp timestamp include report filename.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_reports_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Reports Metadata β generate_reports_meta","text":"return value, called side effects.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Section Content β generate_section_content","title":"Generate Section Content β generate_section_content","text":"Generates HTML content section, including headers enrichment results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Section Content β generate_section_content","text":"","code":"generate_section_content( section_info, index, toc, html_content, section_header_style, toc_style )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Section Content β generate_section_content","text":"section_info list containing information section. index index current section. toc current state Table Contents. html_content current state HTML content. section_header_style CSS style section headers. toc_style CSS style TOC entries.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_section_content.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Section Content β generate_section_content","text":"list updated HTML content TOC.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"function generates spline comparison plots pairwise combinations conditions metadata. condition pair, compares time effects two conditions, plots data points, overlays fitted spline curves. function generates plots adjusted p-values average difference conditions interaction condition time specified thresholds.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"","code":"generate_spline_comparisons( splineomics, all_levels_clustering, data, meta, condition, plot_info, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"splineomics list containing splineomics results, including time effects, average difference conditions, interaction condition time. all_levels_clustering list containing X matrices condition, used spline fitting. data data matrix containing measurements. meta metadata associated measurements, includes condition. condition Column name meta contains levels experiment. plot_info list containing plotting information time unit axis labels. adj_pthresh_avrg_diff_conditions adjusted p-value threshold average difference conditions. adj_pthresh_interaction adjusted p-value threshold interaction condition time. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_spline_comparisons.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate spline comparison plots for all condition pairs β generate_spline_comparisons","text":"list lists containing comparison plots feature names condition pair.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Plots for Time Effect β generate_time_effect_plots","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"Creates p-value histograms time effect LIMMA analysis. function used internally `create_limma_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"","code":"generate_time_effect_plots(time_effect, adj_pthresh)"},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"time_effect list top tables LIMMA analysis representing time effects. adj_pthresh numeric value specifying adjusted p-value threshold significance.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/generate_time_effect_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Plots for Time Effect β generate_time_effect_plots","text":"list containing plots sizes, well section header information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Curve Values Based on Top Table Filter β get_curve_values","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"function filters entries given top table based adjusted p-value threshold, performs spline interpolation using specified degrees freedom, calculates curve values selected entries predefined time points. function internal exported.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"","code":"get_curve_values(top_table, level, meta, condition, spline_params, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"top_table data frame containing data column adjusted p-values expression averages indicate number degrees freedom. level specific level condition filter metadata. meta Metadata containing time points conditions. condition name condition column metadata filter . spline_params list spline parameters analysis. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_curve_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Curve Values Based on Top Table Filter β get_curve_values","text":"list containing two elements: `curve_values`, data frame curve values filtered entry, `smooth_timepoints`, time points curves evaluated.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Plot Explanations β get_explore_plots_explanations","title":"Get Plot Explanations β get_explore_plots_explanations","text":"function returns vector text explanations various types plots. explanations used HTML reports describe plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Plot Explanations β get_explore_plots_explanations","text":"","code":"get_explore_plots_explanations()"},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Plot Explanations β get_explore_plots_explanations","text":"character vector containing explanations different plot types.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_explore_plots_explanations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Plot Explanations β get_explore_plots_explanations","text":"explanations cover variety plots, including density plots, boxplots, violin plots, mean time correlation plots, lag-1 differences plots, first lag autocorrelation plots, coefficient variation (CV) plots, PCA plots, PCA variance explained plots, MDS plots, correlation heatmaps. explanation provides insights plot shows interpret .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Header Section β get_header_section","title":"Get Header Section β get_header_section","text":"Generates HTML header section report, including title, header text, logo. section also includes styling table HTML elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Header Section β get_header_section","text":"","code":"get_header_section(title, header_text, report_type, feature_names_formula)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Header Section β get_header_section","text":"title string specifying title HTML document. header_text string specifying text displayed header report. report_type character specifying type HTML report. feature_names_formula String describing columns annotation info, gene uniprotID, used construct description individual spline plots. placed beginning output HTML reports.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Header Section β get_header_section","text":"string containing HTML header section.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_header_section.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Header Section β get_header_section","text":"function checks `DEVTOOLS_LOAD` environment variable determine path logo image. logo image converted base64 data URI included HTML. header section includes styles tables, table cells, header elements ensure proper formatting alignment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Hit Indices for a Specific Level β get_level_hit_indices","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"function retrieves unique feature indices list -level top tables specified level, based adjusted p-value thresholds.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"","code":"get_level_hit_indices(between_level_top_tables, level, adj_pthresholds)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"between_level_top_tables list data frames containing -level top tables. level string specifying level search within names data frames. adj_pthresholds numeric vector adjusted p-value thresholds data frame `between_level_top_tables`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"vector unique feature indices meet adjusted p-value threshold criteria specified level.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_level_hit_indices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Hit Indices for a Specific Level β get_level_hit_indices","text":"function iterates data frame `between_level_top_tables`. data frame whose name contains specified level (case insensitive), identifies rows adjusted p-value corresponding threshold. function extracts feature indices rows compiles unique list indices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate LIMMA Combination Results β get_limma_combos_results","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"Computes results various combinations data, design matrices, spline configurations using LIMMA method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"","code":"get_limma_combos_results( datas, rna_seq_datas, metas, designs, modes, condition, spline_test_configs, feature_names, adj_pthresholds, padjust_method )"},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"datas list matrices. rna_seq_datas list RNA-seq data objects, voom object derived limma::voom function. metas list metadata corresponding data matrices. designs list design matrices. modes character vector containing 'isolated' 'integrated'. condition single character string specifying condition. spline_test_configs configuration object spline tests. feature_names character vector feature names. adj_pthresholds numeric vector elements > 0 < 1. padjust_method single character string specifying p-adjustment method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_limma_combos_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate LIMMA Combination Results β get_limma_combos_results","text":"list results combination data, design, spline configuration.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Spline Parameters Info β get_spline_params_info","title":"Get Spline Parameters Info β get_spline_params_info","text":"function retrieves spline parameters information given index. ensures spline parameters valid constructs HTML string describing spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Spline Parameters Info β get_spline_params_info","text":"","code":"get_spline_params_info(spline_params, j)"},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Spline Parameters Info β get_spline_params_info","text":"spline_params list containing spline parameters. list include elements: `spline_type`, `degree`, `dof`, `knots`, `bknots`. j integer specifying index spline parameters retrieve.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Spline Parameters Info β get_spline_params_info","text":"character string containing HTML-formatted information spline parameters specified index.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/get_spline_params_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Spline Parameters Info β get_spline_params_info","text":"function checks spline parameters `NULL` length greater equal specified index `j`. parameter invalid missing, sets parameter `NA`. constructs HTML string describing spline parameters, including spline type, degree, degrees freedom (DoF), knots, boundary knots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Data to Hit Comparison Object β hc_add","title":"Add Data to Hit Comparison Object β hc_add","text":"Adds new entry hit comparison object specified condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Data to Hit Comparison Object β hc_add","text":"","code":"hc_add(hc_obj, top_table, params_id, condition = 1, threshold = 0.05)"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Data to Hit Comparison Object β hc_add","text":"hc_obj object class \"hitcomp\". top_table dataframe containing top table data. params_id character string identifying parameters (max length 70). condition integer (1 2) specifying condition data belongs. threshold numeric value specifying adjusted p-value threshold.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_add.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Data to Hit Comparison Object β hc_add","text":"updated hit comparison object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Barplot for Hit Comparison Object β hc_barplot","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"Creates barplot visualize number significant features parameter set hit comparison object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"","code":"hc_barplot(hc_obj)"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"hc_obj object class \"hitcomp\" containing hit data two conditions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_barplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Barplot for Hit Comparison Object β hc_barplot","text":"ggplot2 object representing barplot.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":null,"dir":"Reference","previous_headings":"","what":"Create New Hit Comparison Object β hc_new","title":"Create New Hit Comparison Object β hc_new","text":"Creates new hit comparison object specified condition names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create New Hit Comparison Object β hc_new","text":"","code":"hc_new(cond1name = \"Condition 1\", cond2name = \"Condition 2\")"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create New Hit Comparison Object β hc_new","text":"cond1name character string first condition name (max length 25). cond2name character string second condition name (max length 25).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_new.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create New Hit Comparison Object β hc_new","text":"object class \"hitcomp\" containing empty data lists condition names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Venn Heatmap β hc_vennheatmap","title":"Generate Venn Heatmap β hc_vennheatmap","text":"Creates Venn heatmap visualize overlap hits two conditions stored hit comparison object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Venn Heatmap β hc_vennheatmap","text":"","code":"hc_vennheatmap(hc_obj)"},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Venn Heatmap β hc_vennheatmap","text":"hc_obj object class \"hitcomp\" containing hit data two conditions.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hc_vennheatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Venn Heatmap β hc_vennheatmap","text":"list containing Venn heatmap plot number hits.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Clustering of Curve Values β hierarchical_clustering","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"Performs hierarchical clustering given curve values. function adjusts provided top_table cluster assignments.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"","code":"hierarchical_clustering(curve_values, k, smooth_timepoints, top_table)"},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"curve_values matrix data frame curve values cluster. k number clusters use. smooth_timepoints Numeric vector time points corresponding columns curve_values. top_table Data frame updated cluster assignments.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/hierarchical_clustering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Clustering of Curve Values β hierarchical_clustering","text":"list containing clustering results modified top_table.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"function iterates list tables checks table 300 rows. table found, prompts user proceed stop.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"","code":"huge_table_user_prompter(tables)"},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"tables list data frames.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/huge_table_user_prompter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if any table in a list has more than 300 rows and prompt user for input. β huge_table_user_prompter","text":"NULL. function used side effects (prompting user potentially stopping script).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if Not All Values are NA β is_not_na","title":"Check if Not All Values are NA β is_not_na","text":"Determines given atomic vector contains least one non-NA value.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if Not All Values are NA β is_not_na","text":"","code":"is_not_na(x)"},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if Not All Values are NA β is_not_na","text":"x atomic vector object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/is_not_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if Not All Values are NA β is_not_na","text":"TRUE vector contains least one non-NA value object atomic; FALSE otherwise.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Clustering Report β make_clustering_report","title":"Make Clustering Report β make_clustering_report","text":"Generates detailed clustering report including heatmaps, dendrograms, curve plots, consensus shapes level within condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Clustering Report β make_clustering_report","text":"","code":"make_clustering_report( all_levels_clustering, condition, data, meta, annotation, genes, spline_params, adj_pthresholds, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction_condition_time, report_dir, mode, report_info, design, meta_batch_column, meta_batch2_column, plot_info, plot_options, feature_name_columns, spline_comp_plots, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Clustering Report β make_clustering_report","text":"all_levels_clustering list containing clustering results level within condition. condition character string specifying condition. data matrix data values. meta dataframe containing metadata. annotation Dataframe containig annotation info features, gene uniprotID, example. genes Character vector containing genes features. spline_params list spline parameters analysis. adj_pthresholds Numeric vector, containing float < 1 > 0 value. one float every level, adj. p-value threshold. adj_pthresh_avrg_diff_conditions Float adj_pthresh_interaction_condition_time Float report_dir character string specifying report directory. mode character string specifying mode ('isolated' 'integrated'). report_info object containing report information. design string representing limma design formula meta_batch_column character string specifying meta batch column. meta_batch2_column character string specifying second meta batch column. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). plot_options List specific fields (cluster_heatmap_columns = Bool) allow customization plotting behavior. feature_name_columns Character vector containing column names annotation info describe features. argument used specify HTML report exactly feature names displayed individual spline plot created. Use vector used create row headers data matrix! spline_comp_plots List containing list lists plots pairwise comparisons condition terms average spline diff interaction condition time, another list lists respective names plot stored. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_clustering_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Clustering Report β make_clustering_report","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Correlation Heatmaps β make_correlation_heatmaps","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"function generates correlation heatmaps using Spearman correlation given data matrix. creates combined heatmap levels individual heatmaps level specified condition column metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"","code":"make_correlation_heatmaps(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"data numeric matrix containing data. meta dataframe containing metadata. condition column name metadata dataframe contains factor levels generating individual heatmaps.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_correlation_heatmaps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Correlation Heatmaps β make_correlation_heatmaps","text":"list `ComplexHeatmap` heatmap objects representing correlation heatmaps.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Density Plot β make_density_plots","title":"Generate Density Plot β make_density_plots","text":"function generates density plot given data matrix. density plot shows distribution values data matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Density Plot β make_density_plots","text":"","code":"make_density_plots(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Density Plot β make_density_plots","text":"data numeric matrix containing data. meta dataframe containing column meta data data condition name factor column meta experiment","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_density_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Density Plot β make_density_plots","text":"ggplot object representing density plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Enrich Dotplot β make_enrich_dotplot","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"Make enriched dotplot visualization.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"","code":"make_enrich_dotplot(enrichments_list, databases, title = \"Title\")"},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"enrichments_list list enrichments containing data frames different databases. databases character vector specifying databases included. title character string specifying title dotplot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_enrich_dotplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Enrich Dotplot β make_enrich_dotplot","text":"list containing: p ggplot object representing dotplot. dotplot_nrows integer specifying number rows dotplot. full_enrich_results data frame containing full enrichments results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate MDS Plot β make_mds_plot","title":"Generate MDS Plot β make_mds_plot","text":"function generates multidimensional scaling (MDS) plot given data matrix. MDS plot visualizes similarities dissimilarities samples data matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate MDS Plot β make_mds_plot","text":"","code":"make_mds_plot(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate MDS Plot β make_mds_plot","text":"data numeric matrix containing data. meta dataframe, containign meta information data. condition column meta dataframe containign levels separate experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_mds_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate MDS Plot β make_mds_plot","text":"ggplot object representing MDS plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"function generates PCA plot data matrix, dynamically coloring points based levels specified factor metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"","code":"make_pca_plot(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"data numeric matrix containing data. meta dataframe containing metadata. condition column name metadata dataframe contains factor levels coloring PCA plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_pca_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate PCA Plot with Dynamic Coloring β make_pca_plot","text":"ggplot object representing PCA plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_scatter_plot_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","title":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","text":"Generate scatter plot report rach feature embedded base64 images","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_scatter_plot_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","text":"","code":"make_scatter_plot_html( data, meta, output_file = \"scatter_plots_base64.html\", meta_replicate_column = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/make_scatter_plot_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate scatter plot report for rach feature with embedded base64 images β make_scatter_plot_html","text":"data matrix features rows samples columns. Row names feature names. meta data frame meta information. Must contain numeric column \"Time\". output_file name HTML output file.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Violin Box Plot β make_violin_box_plots","title":"Generate Violin Box Plot β make_violin_box_plots","text":"function generates violin plot given data matrix. violin plot shows distribution values data matrix across different variables, variable's distribution displayed separate violin.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Violin Box Plot β make_violin_box_plots","text":"","code":"make_violin_box_plots(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Violin Box Plot β make_violin_box_plots","text":"data numeric matrix containing data. meta dataframe containing column meta data data condition name factor column meta experiment","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/make_violin_box_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Violin Box Plot β make_violin_box_plots","text":"ggplot object representing violin plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"function manages GSEA analysis specific level. extracts genes associated clustered hits, removes rows `NA` values, runs GSEA analysis using `create_gsea_report` function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"","code":"manage_gsea_level( clustered_hits, level_name, databases, clusterProfiler_params, universe )"},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"clustered_hits dataframe containing clustered hits specific level. must include column named `feature` extract genes. level_name character string representing name level. databases list databases gene set enrichment analysis. clusterProfiler_params Additional parameters GSEA analysis, default NA. include adj_p_value, pAdjustMethod, etc (see clusterProfiler documentation). universe Enrichment background data, default NULL.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/manage_gsea_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manage GSEA Analysis for a Specific Level β manage_gsea_level","text":"result `create_gsea_report` function, typically includes various plots enrichment results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"internal function checks whether valid treatment timepoints labels `plot_info` list. found, adds dashed vertical lines corresponding x-axis values plot. treatment timepoints labels can either named lists (multiple levels) unnamed single elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"","code":"maybe_add_dashed_lines( p, plot_info, level, y_pos = 1, horizontal_labels = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"p ggplot object. plot dashed lines labels added. plot_info list containing treatment timepoints treatment labels. Treatment timepoints labels can either unnamed elements named lists element corresponds different `level`. level character string. Used extract treatment timepoints labels stored named lists. y_pos numeric value specifying y-axis position text labels placed. Defaults 1. horizontal_labels Boolean flag indicating whether vertical label (default) horizontal label.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/maybe_add_dashed_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditionally add dashed lines for treatment timepoints β maybe_add_dashed_lines","text":"list containing: - `p`: ggplot object possibly added dashed lines labels. - `treatment_colors`: named vector colors used treatment labels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"function merges annotation information `top_table` non-logical element list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"","code":"merge_annotation_all_levels_clustering( all_levels_clustering, annotation = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"all_levels_clustering list element contains `top_table` dataframe `feature_nr` column. elements may logical values. annotation dataframe containing annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_annotation_all_levels_clustering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Annotation with All Top Tables β merge_annotation_all_levels_clustering","text":"list updated `top_table` dataframes containing merged annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"function merges annotation information single `top_table` dataframe based `feature_nr` column.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"","code":"merge_top_table_with_annotation(top_table, annotation)"},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"top_table dataframe containing `top_table` `feature_nr` column. annotation dataframe containing annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/merge_top_table_with_annotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge Annotation with a Single Top Table β merge_top_table_with_annotation","text":"dataframe updated `top_table` containing merged annotation information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify limma Top Table β modify_limma_top_table","title":"Modify limma Top Table β modify_limma_top_table","text":"Modifies limma top table include feature indices names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify limma Top Table β modify_limma_top_table","text":"","code":"modify_limma_top_table(top_table, feature_names)"},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify limma Top Table β modify_limma_top_table","text":"top_table dataframe containing top table results limma feature_names character vector feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/modify_limma_top_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Modify limma Top Table β modify_limma_top_table","text":"tibble feature indices names included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize Curve Values β normalize_curves","title":"Normalize Curve Values β normalize_curves","text":"function normalizes row data frame matrix curve values. Normalization performed row's values range 0 (corresponding minimum value row) 1 (corresponding maximum value row).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize Curve Values β normalize_curves","text":"","code":"normalize_curves(curve_values)"},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize Curve Values β normalize_curves","text":"curve_values data frame matrix curve values row represents curve column time point.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/normalize_curves.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize Curve Values β normalize_curves","text":"data frame matrix dimensions input, row normalized.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Open template for SplineOmics analysis for quick setup β open_template","title":"Open template for SplineOmics analysis for quick setup β open_template","text":"function opens `template.Rmd` file RStudio interactive use. template file provides structure users quickly set personal analysis.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Open template for SplineOmics analysis for quick setup β open_template","text":"","code":"open_template()"},{"path":"https://csbg.github.io/SplineOmics/reference/open_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Open template for SplineOmics analysis for quick setup β open_template","text":"successful, opens `template.Rmd` file RStudio user interact . `rstudioapi` installed available, template file found, error thrown corresponding message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_tutorial.html","id":null,"dir":"Reference","previous_headings":"","what":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","title":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","text":"function opens `tutorial.Rmd` file RStudio interactive use. Users can run code chunk step step.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/open_tutorial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","text":"","code":"open_tutorial()"},{"path":"https://csbg.github.io/SplineOmics/reference/open_tutorial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Opens the get-started vignette as an editable .Rmd file in RStudio β open_tutorial","text":"successful, opens `tutorial.Rmd` file RStudio user interact . `rstudioapi` installed available, tutorial file found, error thrown corresponding message.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Clustering β perform_clustering","title":"Perform Clustering β perform_clustering","text":"Performs clustering top tables using specified p-values clusters level within condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Clustering β perform_clustering","text":"","code":"perform_clustering(top_tables, clusters, meta, condition, spline_params, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Clustering β perform_clustering","text":"top_tables list top tables limma analysis. clusters list specifying clusters. meta dataframe containing metadata. condition character string specifying condition. spline_params list spline parameters analysis. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/perform_clustering.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Clustering β perform_clustering","text":"list clustering results level within condition.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Plot to Base64 β plot2base64","title":"Convert Plot to Base64 β plot2base64","text":"Converts ggplot2 plot Base64-encoded PNG image returns HTML img tag embedding report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Plot to Base64 β plot2base64","text":"","code":"plot2base64( plot, height, width = 7, base_height_per_row = 2.5, units = \"in\", html_img_width = \"100%\" )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Plot to Base64 β plot2base64","text":"plot ggplot2 plot object. height integer specifying height plot correct representation HTML. width numeric value specifying width plot inches. base_height_per_row numeric value specifying base height per row inches. units character string specifying units width height. html_img_width character string specifying width image HTML.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot2base64.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Plot to Base64 β plot2base64","text":"character string containing HTML img tag Base64-encoded plot.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot All Mean Splines β plot_all_mean_splines","title":"Plot All Mean Splines β plot_all_mean_splines","text":"Generates plot average curves cluster, showing min-max normalized intensities time.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot All Mean Splines β plot_all_mean_splines","text":"","code":"plot_all_mean_splines(curve_values, plot_info, level)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot All Mean Splines β plot_all_mean_splines","text":"curve_values dataframe containing curve values cluster assignments. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). level One unique values meta condition column. factor separates experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_all_mean_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot All Mean Splines β plot_all_mean_splines","text":"ggplot object representing average curves cluster.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Consensus Shapes β plot_cluster_mean_splines","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"Generates composite plots single consensus shapes cluster curve values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"","code":"plot_cluster_mean_splines(curve_values, plot_info, level)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"curve_values dataframe containing curve values cluster assignments. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cluster_mean_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Consensus Shapes β plot_cluster_mean_splines","text":"list containing plot every cluster","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Composite Splines β plot_composite_splines","title":"Plot Composite Splines β plot_composite_splines","text":"Generates composite spline plots significant non-significant features based specified indices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Composite Splines β plot_composite_splines","text":"","code":"plot_composite_splines( data, meta, spline_test_configs, top_table, top_table_name, indices, type, time_unit_label )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Composite Splines β plot_composite_splines","text":"data matrix data values. meta dataframe containing metadata. spline_test_configs configuration object spline tests. top_table dataframe containing top table results. top_table_name character string specifying name top table. indices vector indices specifying features plot. type character string specifying type features ('significant' 'not_significant'). time_unit_label string shown plots unit time, min hours.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_composite_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Composite Splines β plot_composite_splines","text":"list containing composite plot length plots generated, FALSE otherwise.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of Variation (CV) Plot β plot_cv","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"function takes data frame time series data (rows features columns samples), meta table sample information including time points conditions, computes coefficient variation (CV) feature condition level, plots distribution CVs.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"","code":"plot_cv(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\" condition column. condition name column meta table contains condition information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of Variation (CV) Plot β plot_cv","text":"list ggplot2 objects, showing distribution CVs one condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Dendrogram β plot_dendrogram","title":"Plot Dendrogram β plot_dendrogram","text":"Generates dendrogram plot hierarchical clustering results, colored clusters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Dendrogram β plot_dendrogram","text":"","code":"plot_dendrogram(hc, clusters, k)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Dendrogram β plot_dendrogram","text":"hc hierarchical clustering object. clusters numeric vector, specifying cluster hit . Index 1 cluster hit nr. 1, index 2 hit nr. 2, etc. k integer specifying number clusters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_dendrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Dendrogram β plot_dendrogram","text":"ggplot object representing dendrogram.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"function takes data frame time series data (rows features columns samples), meta table sample information including time points conditions, computes first lag autocorrelation feature condition level, plots distribution autocorrelation coefficients.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"","code":"plot_first_lag_autocorrelation(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\" condition column. condition name column meta table contains condition information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_first_lag_autocorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First Lag Autocorrelation Coefficients Plot β plot_first_lag_autocorrelation","text":"list ggplot2 objects, showing distribution first lag autocorrelation coefficients one condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Heatmap β plot_heatmap","title":"Plot Heatmap β plot_heatmap","text":"Generates heatmaps level within condition, showing z-scores log2 intensity values, split clusters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Heatmap β plot_heatmap","text":"","code":"plot_heatmap( datas, meta, mode, condition, all_levels_clustering, time_unit_label, cluster_heatmap_columns )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Heatmap β plot_heatmap","text":"datas matrix data values. meta dataframe containing metadata. mode character vector length 1, specifying type limma design formula (integrated formulas interaction effects levels, isolated formulas level analysed isolation (interaction effects)) condition character string specifying condition. all_levels_clustering list containing clustering results level within condition. time_unit_label character string specifying time unit label. cluster_heatmap_columns Boolean specifying wether cluster columns heatmap .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Heatmap β plot_heatmap","text":"list ComplexHeatmap heatmap objects level.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":null,"dir":"Reference","previous_headings":"","what":"Lag-1 Differences Plot β plot_lag1_differences","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"function takes data frame time series data (rows features columns samples), meta table sample information including time points conditions, computes lag-1 differences feature condition level, plots distribution differences.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"","code":"plot_lag1_differences(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\" condition column. condition name column meta table contains condition information.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_lag1_differences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lag-1 Differences Plot β plot_lag1_differences","text":"list ggplot2 objects, showing distribution lag-1 differences one condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot limma Combination Results β plot_limma_combos_results","title":"Plot limma Combination Results β plot_limma_combos_results","text":"Generates plots pairwise comparisons hyperparameter combinations using limma results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot limma Combination Results β plot_limma_combos_results","text":"","code":"plot_limma_combos_results( top_tables_combos, datas, metas, condition, spline_test_configs, meta_batch_column, meta_batch2_column, time_unit = time_unit )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot limma Combination Results β plot_limma_combos_results","text":"top_tables_combos list top tables combination. datas list matrices. metas list metadata corresponding data matrices. condition Meta column name contains levels. spline_test_configs configuration object spline tests. meta_batch_column character string specifying meta batch column. meta_batch2_column character string specifying second meta batch column. time_unit single character, s, m, h, d, specifying time_unit used plots (s = seconds, m = minutes, h = hours, d = days). single character converted string little bit verbose, sec square brackets s.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_limma_combos_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot limma Combination Results β plot_limma_combos_results","text":"list results including hit comparison plots composite spline plots pair combinations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"function takes data frame time series data (rows features columns samples) meta table sample information including time points, computes correlation feature time, plots distribution correlations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"","code":"plot_mean_correlation_with_time(data, meta, condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"data data frame rows features columns samples. meta data frame sample metadata. Must contain column \"Time\". condition column meta dataframe containign levels separate experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_mean_correlation_with_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Correlation with Time Plot β plot_mean_correlation_with_time","text":"ggplot2 object showing distribution mean correlations time. @importFrom rlang .data","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Single and Mean Splines β plot_single_and_mean_splines","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"Generates plot showing individual time series shapes consensus (mean) shape.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"","code":"plot_single_and_mean_splines(time_series_data, title, plot_info, level)"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"time_series_data dataframe matrix time series data. title character string specifying title plot. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_single_and_mean_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Single and Mean Splines β plot_single_and_mean_splines","text":"ggplot object representing single consensus shapes.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Create spline comparison plots for two conditions β plot_spline_comparisons","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"function generates comparison plots spline fits two conditions time. compares time effects two conditions, plots data points, overlays fitted spline curves. function checks adjusted p-values average difference conditions interaction condition time specified thresholds generating plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"","code":"plot_spline_comparisons( time_effect_1, condition_1, time_effect_2, condition_2, avrg_diff_conditions, interaction_condition_time, data, meta, condition, X_1, X_2, plot_info, adj_pthresh_avrg_diff_conditions, adj_pthresh_interaction, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"time_effect_1 data frame containing time effects first condition. condition_1 name first condition. time_effect_2 data frame containing time effects second condition. condition_2 name second condition. avrg_diff_conditions data frame adjusted p-values average difference conditions. interaction_condition_time data frame adjusted p-values interaction condition time. data data matrix containing measurements. meta metadata associated measurements. condition Column name meta contains levels experiment. X_1 matrix spline basis values first condition. X_2 matrix spline basis values second condition. plot_info list containing plotting information time unit axis labels. adj_pthresh_avrg_diff_conditions adjusted p-value threshold average difference conditions. adj_pthresh_interaction adjusted p-value threshold interaction condition time. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_spline_comparisons.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create spline comparison plots for two conditions β plot_spline_comparisons","text":"list containing: plots list ggplot2 plots comparing two conditions. feature_names list feature names plotted features.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Splines for Features Based on Top Table Information β plot_splines","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"function generates plots feature listed top table using spline interpolation fitted values. creates individual plots feature combines single composite plot.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"","code":"plot_splines( top_table, data, meta, X, time_unit_label, plot_info, adj_pthreshold, replicate_column, level, raw_data )"},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"top_table dataframe containing indices names features, along statistical metrics intercepts spline coefficients. data matrix dataframe containing raw data values feature. meta dataframe containing metadata data, including time points. X limma design matrix defines experimental conditions. time_unit_label string shown plots unit time, min hours. plot_info List containing elements y_axis_label (string), time_unit (string), treatment_labels (character vector), treatment_timepoints (integer vector). can also NA. list used add info spline plots. time_unit used label x-axis, treatment_labels -timepoints used create vertical dashed lines, indicating positions treatments (feeding, temperature shift, etc.). adj_pthreshold Double > 0 < 1 specifying adj. p-val threshold. replicate_column String specifying column meta dataframe contains labels replicate measurents. given, argument NULL. level Unique value meta condition column, 'treatment' 'control'. raw_data Optional. Data matrix raw (unimputed) data, still containing NA values. provided, highlights datapoints spline plots originally NA imputed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/plot_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Splines for Features Based on Top Table Information β plot_splines","text":"list containing composite plot number rows used plot layout.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"function processes clustered hits element `all_levels_clustering`, formats gene names easy copy-pasting Enrichr, returns formatted gene lists string.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"","code":"prepare_gene_lists_for_enrichr(all_levels_clustering, genes)"},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"all_levels_clustering list element contains dataframe `clustered_hits` columns `feature` `cluster`. genes vector gene names corresponding feature indices.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_gene_lists_for_enrichr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare Gene Lists for Enrichr and Return as String β prepare_gene_lists_for_enrichr","text":"character vector formatted gene lists cluster.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Plot Data β prepare_plot_data","title":"Prepare Plot Data β prepare_plot_data","text":"function prepares plot data visualization based enrichments lists specified databases.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Plot Data β prepare_plot_data","text":"","code":"prepare_plot_data(enrichments_list, databases)"},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Plot Data β prepare_plot_data","text":"enrichments_list list enrichments containing data frames different databases. databases character vector specifying databases included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/prepare_plot_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare Plot Data β prepare_plot_data","text":"list containing two data frames: top_plot_data data frame containing prepared plot data visualization top combinations. full_enrich_results data frame containing full enrichments results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"`preprocess_rna_seq_data()` function performs essential preprocessing steps raw RNA-seq counts. includes creating `DGEList` object, normalizing counts using default TMM (Trimmed Mean M-values) normalization via `edgeR::calcNormFactors` function, applying `voom` transformation `limma` package obtain log-transformed counts per million (logCPM) associated precision weights. require different normalization method, can supply custom normalization function.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"","code":"preprocess_rna_seq_data( raw_counts, meta, spline_params, design, normalize_func = NULL )"},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"raw_counts matrix raw RNA-seq counts (genes rows, samples columns). meta dataframe containing metadata data. spline_params Parameters spline functions (optional). Must contain named elements spline_type, must contain either string \"n\" natural cubic splines, \"b\", B-splines, named element degree case B-splines, must contain integer, named element dof, specifying degree freedom, containing integer required natural B-splines. design design formula limma analysis, '~ 1 + Phase*X + Reactor'. normalize_func optional normalization function. provided, function used normalize `DGEList` object. provided, TMM normalization (via `edgeR::calcNormFactors`) used default. Must take input y : y <- edgeR::DGEList(counts = raw_counts) output y normalized counts.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/preprocess_rna_seq_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform default preprocessing of raw RNA-seq counts using limma::voom β preprocess_rna_seq_data","text":"`voom` object, includes log2-counts per million (logCPM) matrix observation-specific weights.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":null,"dir":"Reference","previous_headings":"","what":"Print function for SplineOmics objects β print.SplineOmics","title":"Print function for SplineOmics objects β print.SplineOmics","text":"function provides summary print SplineOmics object, showing relevant information number features, samples, metadata, RNA-seq data, annotation, spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print function for SplineOmics objects β print.SplineOmics","text":"","code":"# S3 method for class 'SplineOmics' print(x, ...)"},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print function for SplineOmics objects β print.SplineOmics","text":"x SplineOmics object created `create_splineomics` function. ... Additional arguments passed methods.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print function for SplineOmics objects β print.SplineOmics","text":"function return value. prints summary SplineOmics object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print.SplineOmics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print function for SplineOmics objects β print.SplineOmics","text":"function automatically called SplineOmics object printed. provides concise overview object's contents attributes, including dimensions data, available metadata, relevant information annotations spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print_info_message.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Informational Message β print_info_message","title":"Print Informational Message β print_info_message","text":"function prints nicely formatted informational message green \"Info\" label.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/print_info_message.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Informational Message β print_info_message","text":"","code":"print_info_message(message_prefix, report_dir)"},{"path":"https://csbg.github.io/SplineOmics/reference/print_info_message.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Informational Message β print_info_message","text":"message_prefix custom message prefix displayed success message. report_dir directory HTML reports located.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Combination β process_combo","title":"Process Combination β process_combo","text":"Processes single combination data, design, spline configuration, p-threshold generate LIMMA spline results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Combination β process_combo","text":"","code":"process_combo( data_index, design_index, spline_config_index, pthreshold, datas, rna_seq_datas, metas, designs, modes, condition, spline_test_configs, feature_names, padjust_method, ... )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Combination β process_combo","text":"data_index Index data datas list. design_index Index design designs list. spline_config_index Index spline configuration spline_test_configs list. pthreshold p-value threshold significance. datas list data matrices rna_seq_datas list RNA-seq data objects, voom object derived limma::voom function. metas list metadata corresponding data matrices. designs list design matrices. modes character vector containing 'isolated' 'integrated'. condition single character string specifying condition. spline_test_configs configuration object spline tests. feature_names character vector feature names. padjust_method single character string specifying p-adjustment method. ... Additional arguments.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Combination β process_combo","text":"list top tables LIMMA spline analysis.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Combination Pair β process_combo_pair","title":"Process Combination Pair β process_combo_pair","text":"Processes combination pair generate plots compile HTML report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Combination Pair β process_combo_pair","text":"","code":"process_combo_pair( combo_pair, combo_pair_name, report_info, report_dir, timestamp )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Combination Pair β process_combo_pair","text":"combo_pair list containing hit comparison composite spline plots. combo_pair_name character string naming combination pair. report_info object containing report information. report_dir non-empty string specifying report directory. timestamp timestamp include report filename.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_combo_pair.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Combination Pair β process_combo_pair","text":"return value, called side effects.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Configuration Column β process_config_column","title":"Process Configuration Column β process_config_column","text":"Processes configuration column based given mode number levels.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Configuration Column β process_config_column","text":"","code":"process_config_column(config_column, index, num_levels, mode)"},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Configuration Column β process_config_column","text":"config_column configuration column spline test configurations. index Index configuration process. num_levels Number unique levels metadata condition. mode character string specifying mode ('integrated' 'isolated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_config_column.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Configuration Column β process_config_column","text":"vector list processed configuration values.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Enrichment Results β process_enrichment_results","title":"Process Enrichment Results β process_enrichment_results","text":"Process enrichment results visualization.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Enrichment Results β process_enrichment_results","text":"","code":"process_enrichment_results( all_db_results, enrichment_results, adjP_threshold, column_name, count_column_name, background = FALSE )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Enrichment Results β process_enrichment_results","text":"all_db_results list data frames containing enrichment results databases. enrichment_results list data frames containing enrichment results individual databases. adjP_threshold threshold adjusted p-values. column_name name column store adjusted p-values. count_column_name name column store gene counts. background Logical indicating whether background ratios included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_enrichment_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Enrichment Results β process_enrichment_results","text":"list data frames containing processed enrichment results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":null,"dir":"Reference","previous_headings":"","what":"Process and Encode Data Field for Report β process_field","title":"Process and Encode Data Field for Report β process_field","text":"function processes given field, encodes associated data base64, generates download link report. handles different types fields including data, meta, top tables, Enrichr formatted gene lists.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process and Encode Data Field for Report β process_field","text":"","code":"process_field( field, data, meta, topTables, report_info, encode_df_to_base64, enrichr_format )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process and Encode Data Field for Report β process_field","text":"field string specifying field process. data dataframe containing main data. meta dataframe containing meta information. topTables dataframe containing results differential expression analysis. report_info list containing additional report information. encode_df_to_base64 function encode dataframe base64. enrichr_format list formatted gene lists background gene list.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_field.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process and Encode Data Field for Report β process_field","text":"string containing HTML link downloading processed field.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Level Cluster β process_level_cluster","title":"Process Level Cluster β process_level_cluster","text":"Processes clustering specific level within condition using provided top table spline parameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Level Cluster β process_level_cluster","text":"","code":"process_level_cluster( top_table, cluster_size, level, meta, condition, spline_params, mode )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Level Cluster β process_level_cluster","text":"top_table dataframe containing top table results limma. cluster_size size clusters generate. level level within condition process. meta dataframe containing metadata. condition character string specifying condition. spline_params list spline parameters analysis. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_level_cluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Level Cluster β process_level_cluster","text":"list containing clustering results, including curve values design matrix.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Plots β process_plots","title":"Process Plots β process_plots","text":"Converts plots base64 appends HTML content.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Plots β process_plots","text":"","code":"process_plots( plots_element, plots_size, html_content, toc, header_index, element_name = NA )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Plots β process_plots","text":"plots_element list plots processed. plots_size list sizes plots. html_content current state HTML content. toc current state table contents (TOC). header_index index uniquely identify section anchoring. element_name character string specifying name element.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_plots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Plots β process_plots","text":"Updated HTML content plots included.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":null,"dir":"Reference","previous_headings":"","what":"Process GSEA Result for a Specific Level β process_result","title":"Process GSEA Result for a Specific Level β process_result","text":"function processes GSEA result specific level. handles cases result contains `NA` values adding section break. Otherwise, extracts plot, plot size, header information result.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process GSEA Result for a Specific Level β process_result","text":"","code":"process_result(level_result, level_name)"},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process GSEA Result for a Specific Level β process_result","text":"level_result list containing GSEA result specific level. level_name character string representing name level.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_result.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process GSEA Result for a Specific Level β process_result","text":"list following components: plot plot object \"section_break\" result contains `NA`. plot_size integer indicating size plot. header_info list header information, including level name, full enrichment results, raw enrichment results available.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Top Table β process_top_table","title":"Process Top Table β process_top_table","text":"Processes top table LIMMA analysis, adding feature names intercepts.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Top Table β process_top_table","text":"","code":"process_top_table(process_within_level_result, feature_names)"},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Top Table β process_top_table","text":"process_within_level_result List lists containing limma topTable, fit. one specific level. feature_names non-empty character vector feature names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_top_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Top Table β process_top_table","text":"dataframe containing processed top table added intercepts.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Within Level β process_within_level","title":"Process Within Level β process_within_level","text":"Performs within-level analysis using limma generate top tables fit objects based specified spline parameters. Performs limma spline analysis selected level factor","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Within Level β process_within_level","text":"","code":"process_within_level( data, rna_seq_data, meta, design, dream_params, spline_params, level_index, padjust_method )"},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Within Level β process_within_level","text":"data matrix data values. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta dataframe containing metadata, including 'Time' column. design design formula matrix limma analysis. dream_params named list NULL. NULL, must least contain named element 'random_effects', must contain string formula random effects mixed models dream. Additionally, can contain named elements dof, must int bigger 1, degree freedom dream topTable, named element KenwardRoger, must bool, specifying whether use method . spline_params list spline parameters analysis. level_index index level within factor. padjust_method character string specifying p-adjustment method.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/process_within_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Within Level β process_within_level","text":"list containing top table fit object limma analysis.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":null,"dir":"Reference","previous_headings":"","what":"Read and split section texts from a file β read_section_texts","title":"Read and split section texts from a file β read_section_texts","text":"internal function reads contents text file located `inst/descriptions` directory package splits individual sections based specified delimiter.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read and split section texts from a file β read_section_texts","text":"","code":"read_section_texts(filename)"},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read and split section texts from a file β read_section_texts","text":"filename character string specifying name file containing section texts. file located `inst/descriptions` directory package.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/read_section_texts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read and split section texts from a file β read_section_texts","text":"character vector element section text split delimiter `|`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Batch Effect β remove_batch_effect","title":"Remove Batch Effect β remove_batch_effect","text":"Removes batch effects data matrices using specified batch column metadata.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Batch Effect β remove_batch_effect","text":"","code":"remove_batch_effect( datas, metas, meta_batch_column, meta_batch2_column, condition )"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Batch Effect β remove_batch_effect","text":"datas list matrices. metas list metadata corresponding data matrices. meta_batch_column character string specifying meta batch column. meta_batch2_column character string specifying second meta batch column. condition character vector length 1, specifying column name meta dataframe, contains levels separate experiment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Batch Effect β remove_batch_effect","text":"list matrices batch effects removed applicable.","code":""},{"path":[]},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"function removes batch effects data level specified condition. supports isolated integrated modes, optional handling second batch column.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"","code":"remove_batch_effect_cluster_hits( data, meta, condition, meta_batch_column, meta_batch2_column, design, mode, spline_params )"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"data dataframe containing main data. meta dataframe containing meta information. condition string specifying column `meta` divides experiment levels. meta_batch_column string specifying column `meta` indicates batch information. meta_batch2_column string specifying second batch column `meta`, applicable. design design matrix experiment. mode string indicating mode operation: \"isolated\" \"integrated\". spline_params list spline parameters design matrix.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"list dataframes batch effects removed level.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_batch_effect_cluster_hits.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove Batch Effect from Cluster Hits β remove_batch_effect_cluster_hits","text":"function operates two modes: isolated Processes level independently, using data level. integrated Processes entire dataset together. `meta_batch_column` specified, function removes batch effects using `removeBatchEffect`. second batch column (`meta_batch2_column`) specified, also included batch effect removal.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove intercept from a formula β remove_intercept","title":"Remove intercept from a formula β remove_intercept","text":"function modifies given formula replacing first occurrence standalone intercept (`1`) `0`. works even `1` preceded tilde (`~`), ensuring intercept removed leaving parts formula intact.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove intercept from a formula β remove_intercept","text":"","code":"remove_intercept(formula)"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove intercept from a formula β remove_intercept","text":"formula formula object. formula can include intercept (`1`) terms. `1` found, replaced `0`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_intercept.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove intercept from a formula β remove_intercept","text":"modified formula intercept removed. first standalone occurrence `1` replaced `0`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Prefix from String β remove_prefix","title":"Remove Prefix from String β remove_prefix","text":"Removes specified prefix beginning string. function useful cleaning standardizing strings removing known prefixes.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Prefix from String β remove_prefix","text":"","code":"remove_prefix(string, prefix)"},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Prefix from String β remove_prefix","text":"string string prefix removed. prefix string representing prefix removed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/remove_prefix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove Prefix from String β remove_prefix","text":"string prefix removed.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"function generates Gene Set Enrichment Analysis (GSEA) report based clustered hit levels, gene data, specified databases. processes input data, manages GSEA levels, produces HTML report plots.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"","code":"run_gsea( levels_clustered_hits, databases, report_info, clusterProfiler_params = NA, plot_titles = NA, universe = NULL, report_dir = here::here() )"},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"levels_clustered_hits list clustered hits different levels. databases list databases gene set enrichment analysis. report_info list containing information report generation. clusterProfiler_params Additional parameters GSEA analysis, default NA. include adj_p_value, pAdjustMethod, etc (see clusterProfiler documentation). plot_titles Titles plots, default NA. universe Enrichment background data, default NULL. report_dir Directory report saved, default `::()`.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_gsea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs gene set enrichment analysis of the clustered hits using clusterProfiler β run_gsea","text":"list plots generated GSEA report.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":null,"dir":"Reference","previous_headings":"","what":"Central function of the package that runs a limma analysis with splines β run_limma_splines","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"function performs limma spline analysis identify significant time-dependent changes features (e.g., proteins) within omics time-series dataset. evaluates features within condition level levels comparing average differences interactions time condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"","code":"run_limma_splines(splineomics)"},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"splineomics S3 object class `SplineOmics` contains following elements: data: matrix omics dataset, feature names optionally row headers. rna_seq_data: object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta: dataframe containing metadata corresponding data, must include 'Time' column column specified condition. design: character string representing limma design formula. condition: character string specifying column name meta used define groups analysis. spline_params: list spline parameters used analysis, including: spline_type: type spline (e.g., \"n\" natural splines \"b\" B-splines). dof: Degrees freedom spline. knots: Positions internal knots (B-splines). bknots: Boundary knots (B-splines). degree: Degree spline (B-splines ).","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/run_limma_splines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Central function of the package that runs a limma analysis with splines β run_limma_splines","text":"SplineOmics object, updated list three elements: - `time_effect`: list top tables level time effect. - `avrg_diff_conditions`: list top tables comparison levels. comparison average difference values. - `interaction_condition_time`: list top tables comparison levels. comparison interaction condition time.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":null,"dir":"Reference","previous_headings":"","what":"Allows to test different ","title":"Allows to test different ","text":"function screens various combinations hyperparameters limma analysis, including designs, modes, degrees freedom. validates inputs, generates results combinations, plots outcomes. Finally, may also involved generating HTML report part larger analysis workflow.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allows to test different ","text":"","code":"screen_limma_hyperparams( splineomics, datas, datas_descr, metas, designs, modes, spline_test_configs, report_dir = here::here(), adj_pthresholds = c(0.05), rna_seq_datas = NULL, time_unit = \"min\", padjust_method = \"BH\" )"},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allows to test different ","text":"splineomics S3 object class `SplineOmics` contains necessary data parameters analysis, including: condition: string specifying column name meta dataframe, contains levels separate experiment ('treatment' can condition, 'drug' 'drug' can levels condition). report_info: meta_batch_column: character string specifying meta batch column. meta_batch2_column: character string specifying second meta batch column (limma function removeBatchEffect supports maximum two batch columns.) datas list matrices containing datasets analyzed. datas_descr description object data. metas list data frames containing metadata dataset `datas`. designs character vector design formulas limma analysis. modes character vector must length 'designs'. design formula, must specify either 'isolated' 'integrated'. Isolated means limma determines results level using data level. Integrated means limma determines results levels using full dataset (levels). spline_test_configs configuration object spline tests. report_dir non-empty string specifying report directory. adj_pthresholds numeric vector p-value thresholds significance determination. rna_seq_datas list RNA-seq data objects, voom object derived limma::voom function. time_unit character string specifying time unit label plots. padjust_method character string specifying method p-value adjustment.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/screen_limma_hyperparams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Allows to test different ","text":"Returns list plots generated limma analysis results. element list corresponds different combination hyperparameters.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Default Parameters β set_default_params","title":"Set Default Parameters β set_default_params","text":"function checks provided `params` list `NA` missing elements. `params` `NA`, assigns list default parameters. element missing `params`, adds missing element respective default value.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Default Parameters β set_default_params","text":"","code":"set_default_params(params)"},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Default Parameters β set_default_params","text":"params list parameters checked updated default values necessary.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/set_default_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Default Parameters β set_default_params","text":"list parameters required elements, either input `params` added default values missing elements.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Shorten Names β shorten_names","title":"Shorten Names β shorten_names","text":"Replaces occurrences unique values within name first three characters. function useful abbreviating long condition names dataset.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shorten Names β shorten_names","text":"","code":"shorten_names(name, unique_values)"},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shorten Names β shorten_names","text":"name string representing name shortened. unique_values vector unique values whose abbreviations replace occurrences name.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/shorten_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shorten Names β shorten_names","text":"string unique values replaced abbreviations.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":null,"dir":"Reference","previous_headings":"","what":"Stop with custom message without call. β stop_call_false","title":"Stop with custom message without call. β stop_call_false","text":"helper function triggers error specified message suppresses function call error output. function behaves similarly base `stop()` function automatically concatenates multiple message strings provided.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stop with custom message without call. β stop_call_false","text":"","code":"stop_call_false(...)"},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stop with custom message without call. β stop_call_false","text":"... One character strings specifying error message. multiple strings provided, concatenated space .","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/stop_call_false.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stop with custom message without call. β stop_call_false","text":"function return value; stops execution throws error.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":null,"dir":"Reference","previous_headings":"","what":"Store Hits β store_hits","title":"Store Hits β store_hits","text":"Stores feature indices significant hits based adjusted p-value threshold condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Store Hits β store_hits","text":"","code":"store_hits(condition)"},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Store Hits β store_hits","text":"condition list containing dataframes parameters condition.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/store_hits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Store Hits β store_hits","text":"list element vector feature indices meet significance threshold.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Truncate Row Names β truncate_row_names","title":"Truncate Row Names β truncate_row_names","text":"function truncates row names exceed specified maximum length. row name length exceeds maximum length, appends \" ...\" indicate truncation.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Truncate Row Names β truncate_row_names","text":"","code":"truncate_row_names(names, max_length = 40)"},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Truncate Row Names β truncate_row_names","text":"names character vector row names. max_length integer specifying maximum length row names. Default 40.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/truncate_row_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Truncate Row Names β truncate_row_names","text":"character vector truncated row names.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":null,"dir":"Reference","previous_headings":"","what":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"Updates SplineOmics object modifying existing fields adding new ones.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"","code":"update_splineomics(splineomics, ...)"},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"splineomics SplineOmics object updated. ... Named arguments new values fields updated added.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/update_splineomics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update a SplineOmics object with new values for stored variables or add new variables β update_splineomics","text":"updated SplineOmics object.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Within level analysis β within_level","title":"Within level analysis β within_level","text":"Processes single level within condition, performing limma analysis generating top table results.","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Within level analysis β within_level","text":"","code":"within_level( level, level_index, spline_params, data, rna_seq_data, meta, design, dream_params, condition, feature_names, padjust_method, mode )"},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Within level analysis β within_level","text":"level level within condition process. level_index index level within condition. spline_params list spline parameters analysis. data matrix data values. rna_seq_data object containing preprocessed RNA-seq data, output `limma::voom` similar preprocessing pipeline. meta dataframe containing metadata data. design design formula matrix limma analysis. dream_params named list NULL. NULL, must least contain named element 'random_effects', must contain string formula random effects mixed models dream. Additionally, can contain named elements dof, must int bigger 1, degree freedom dream topTable, named element KenwardRoger, must bool, specifying whether use method . condition character string specifying condition. feature_names non-empty character vector feature names. padjust_method character string specifying p-adjustment method. mode character string specifying mode ('isolated' 'integrated').","code":""},{"path":"https://csbg.github.io/SplineOmics/reference/within_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Within level analysis β within_level","text":"list containing name results top table results.","code":""},{"path":[]}]
diff --git a/vignettes/RNA-seq analysis.Rmd b/vignettes/RNA-seq analysis.Rmd
index 80612e9..df306c2 100755
--- a/vignettes/RNA-seq analysis.Rmd
+++ b/vignettes/RNA-seq analysis.Rmd
@@ -411,10 +411,10 @@ contains an interaction effect).
# Perform gene set enrichment analysis (GSEA)
-Usually, the final step in such a bioinformatics analysis is GSEA. To
-each clustered hit, the respective gene can be assigned and GSEA
-performed. For this, the Enrichr databases of choice have to be
-downloaded:
+Once the clustered hits are identified, a subsequent step to gain biological
+insights is to perform GSEA. For this, the respective genes can be assigned to
+each clustered hit, and GSEA can be carried out. To proceed, the Enrichr
+databases of choice need to be downloaded:
```{r download Enrichr databases, eval = FALSE}
# Specify which databases you want to download from Enrichr
diff --git a/vignettes/get-started.Rmd b/vignettes/get-started.Rmd
index 8524cba..091d0e9 100755
--- a/vignettes/get-started.Rmd
+++ b/vignettes/get-started.Rmd
@@ -775,10 +775,10 @@ contains an interaction effect).
# Perform gene set enrichment analysis (GSEA)
-Usually, the final step in such a bioinformatics analysis is GSEA. To
-each clustered hit, the respective gene can be assigned and GSEA
-performed. For this, the Enrichr databases of choice have to be
-downloaded:
+Once the clustered hits are identified, a subsequent step to gain biological
+insights is to perform GSEA. For this, the respective genes can be assigned to
+each clustered hit, and GSEA can be carried out. To proceed, the Enrichr
+databases of choice need to be downloaded:
```{r download Enrichr databases, eval = FALSE}
# Specify which databases you want to download from Enrichr