From 2994a2ae52a42d48ca60bcbab142a3c2be5811ea Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Sun, 16 Jun 2024 09:21:58 +0000 Subject: [PATCH] Built site for pRoloc@1.45.1: c1a22cc --- 404.html | 18 +- CONDUCT.html | 18 +- articles/index.html | 18 +- articles/v01-pRoloc-tutorial.html | 350 ++++++++-------- .../figure-html/params2-1.png | Bin 23784 -> 22280 bytes .../figure-html/plotHclust-1.png | Bin 228797 -> 220045 bytes .../figure-html/plotKmeans-1.png | Bin 236176 -> 215459 bytes .../figure-html/plotPam-1.png | Bin 349912 -> 341418 bytes .../figure-html/plotavrprof-1.png | Bin 49089 -> 48539 bytes .../figure-html/qsep-1.png | Bin 243068 -> 234904 bytes articles/v02-pRoloc-ml.html | 389 ++++++++++-------- articles/v03-pRoloc-bayesian.html | 21 +- articles/v04-pRoloc-goannotations.html | 53 +-- .../figure-html/visualiseRes-1.png | Bin 163991 -> 159944 bytes .../figure-html/visualiseRes-2.png | Bin 190155 -> 188797 bytes articles/v05-pRoloc-transfer-learning.html | 317 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+- reference/zerosInBinMSnSet-1.png | Bin 56235 -> 51945 bytes reference/zerosInBinMSnSet.html | 34 +- sitemap.xml | 376 +++++------------ 134 files changed, 2546 insertions(+), 2720 deletions(-) diff --git a/404.html b/404.html index 9096deb7..e36390a9 100644 --- a/404.html +++ b/404.html @@ -6,7 +6,7 @@ Page not found (404) • pRoloc - + @@ -18,7 +18,7 @@ - +
@@ -44,7 +44,7 @@
- +
@@ -112,16 +112,16 @@

Page not found (404)

-

Site built with pkgdown 2.0.7.9000.

+

Site built with pkgdown 2.0.9.9000.

- - + + diff --git a/CONDUCT.html b/CONDUCT.html index c0ebbb40..6ae19548 100644 --- a/CONDUCT.html +++ b/CONDUCT.html @@ -1,9 +1,9 @@ -Contributor Code of Conduct • pRolocContributor Code of Conduct • pRoloc - +
@@ -28,7 +28,7 @@
- +
@@ -93,15 +93,15 @@

Contributor Code of Conduct

-

Site built with pkgdown 2.0.7.9000.

+

Site built with pkgdown 2.0.9.9000.

- - + + diff --git a/articles/index.html b/articles/index.html index a65a8d67..7c39ffe7 100644 --- a/articles/index.html +++ b/articles/index.html @@ -1,9 +1,9 @@ -Articles • pRolocArticles • pRoloc - +
@@ -28,7 +28,7 @@
- +
@@ -92,15 +92,15 @@

All vignettes

-

Site built with pkgdown 2.0.7.9000.

+

Site built with pkgdown 2.0.9.9000.

- - + + diff --git a/articles/v01-pRoloc-tutorial.html b/articles/v01-pRoloc-tutorial.html index 6d5fdf2b..4f7e0e74 100644 --- a/articles/v01-pRoloc-tutorial.html +++ b/articles/v01-pRoloc-tutorial.html @@ -6,20 +6,19 @@ Using pRoloc for spatial proteomics data analysis • pRoloc - + - - +
@@ -45,7 +44,7 @@
- +
@@ -99,7 +98,7 @@

Lisa M. UK

Laurent Gatto

- de Duve Institute, UCLouvain, Belgium
Source: vignettes/v01-pRoloc-tutorial.Rmd + de Duve Institute, UCLouvain, Belgium
Source: vignettes/v01-pRoloc-tutorial.Rmd
@@ -209,7 +208,7 @@

Foreword

Questions and bugs

-

You are welcome to contact me directly about pRoloc. +

You are welcome to contact me directly about pRoloc. For bugs, typos, suggestions or other questions, please file an issue in our issue tracking system (https://github.com/lgatto/pRoloc/issues) providing as much information as possible, a reproducible example and the output of @@ -230,10 +229,10 @@

Spatial proteomicspRoloc, +monitor single cells, as the former is the primary concern of pRoloc, although the techniques described below and the infrastructure in place could also be applied the processed image data. The typical experimental -use-case for using pRoloc is +use-case for using pRoloc is a set of fractions, originating from a total cell lysate. These fractions can originate from a continuous gradient, like in the LOPIT (Dunkley et al. 2006) or PCP (Foster et al. 2006) approaches, or can be @@ -258,8 +257,8 @@

About R and pRoloc(Gentleman et al. 2004). Two packages are of -particular interest here, namely MSnbase -(Gatto and Lilley 2012) and pRoloc. +particular interest here, namely MSnbase +(Gatto and Lilley 2012) and pRoloc. The former provides flexible infrastructure to store and manipulate quantitative proteomics data and the associated meta-data and the latter implements specific algorithmic technologies to analyse organelle @@ -284,9 +283,9 @@

About R and pRoloclibrary("MSnbase") library("pRoloc") library("pRolocdata")

-

MSnbase -implements the data containers that are used by pRoloc. -pRolocdata +

MSnbase +implements the data containers that are used by pRoloc. +pRolocdata is a data package that supplies several published organelle proteomics data sets.

As a final setup step, we set the default colour palette for some of @@ -431,7 +430,7 @@

From csv files to R data## 3 X116 19 ## 4 X117 21

A self-contained data structure, called MSnSet (defined in -the MSnbase +the MSnbase package) can now easily be generated using the readMSnSet constructor, providing the respective csv file names shown above and specifying that the data is comma-separated (with @@ -458,8 +457,8 @@

From csv files to R data## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - -## Quantitation data loaded: Tue Mar 12 07:14:54 2024 using readMSnSet. -## MSnbase version: 2.29.3 +## Quantitation data loaded: Sun Jun 16 09:19:10 2024 using readMSnSet. +## MSnbase version: 2.31.1

@@ -494,7 +493,7 @@

A shorter input work flow## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - -## MSnbase version: 2.29.3 +## MSnbase version: 2.31.1

The ecol columns can also be queried interactively from R using the getEcols and grepEcols function. The former return a character with all column names, given a splitting @@ -530,7 +529,7 @@

A shorter input work flow## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - -## MSnbase version: 2.29.3 +## MSnbase version: 2.31.1

The phenoData slot can now be updated accordingly using the replacement functions phenoData<- or pData<- (see ?MSnSet for details).

@@ -549,7 +548,7 @@

The MSnSet class -

It is common, in the context of pRoloc to +

It is common, in the context of pRoloc to update the feature meta-data (described in section @ref(sec:analysis)) by adding new columns, without breaking the objects validity. Similarly, the sample meta-data can also be updated by adding new sample variables. @@ -584,7 +583,7 @@

The MSnSet class## FBgn0003731 0.247500 0.253000 ## FBgn0029506 0.216000 0.183000

Several data sets, including the 3 replicates from (Tan et al. 2009), are distributed as -MSnSet instances in the pRolocdata +MSnSet instances in the pRolocdata package. Others include, among others, the Arabidopsis thaliana LOPIT data from (Dunkley et al. 2006) (dunkley2006) and the mouse PCP data from (Foster et al. 2006) (foster2006). @@ -616,7 +615,7 @@

The MSnSet class## 235 74 180 399

Important As can be seen above, some proteins are labelled "unknown", defining non marker proteins. This is a -convention in many pRoloc +convention in many pRoloc functions. Missing annotations (empty string) will not be considered as of unknown localisation; we prefer to avoid empty strings and make the absence of known localisation explicit by using the @@ -630,9 +629,9 @@

The MSnSet class pRoloc’s organelle markers

-

The pRoloc +

The pRoloc package distributes a set of markers that have been obtained by mining -the pRolocdata +the pRolocdata datasets and curation by various members of the Cambridge Centre for Proteomics. The available marker sets can be obtained and loaded using the pRolocmarkers function:

@@ -684,9 +683,9 @@

Data processing## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.9990 0.9999 1.0000 1.0000 1.0001 1.0010

The normalise method (also available as -normalize) from the MSnbase +normalize) from the MSnbase package can be used to obtain relative quantitation data, as illustrated -below on another iTRAQ test data set, available from MSnbase. +below on another iTRAQ test data set, available from MSnbase. Several normalisation methods are available and described in ?normalise. For many algorithms, including classifiers in general and support vector machines in particular, it is important to @@ -718,8 +717,8 @@

Data processing## - - - Processing information - - - ## Data loaded: Wed May 11 18:54:39 2011 ## Updated from version 0.3.0 to 0.3.1 [Fri Jul 8 20:23:25 2016] -## iTRAQ4 quantification by trapezoidation: Tue Mar 12 07:14:58 2024 -## Normalised (sum): Tue Mar 12 07:14:58 2024 +## iTRAQ4 quantification by trapezoidation: Sun Jun 16 09:19:15 2024 +## Normalised (sum): Sun Jun 16 09:19:15 2024 ## MSnbase version: 1.1.22
 head(exprs(itraqnorm), n = 3)
@@ -736,9 +735,10 @@

Data processingprocessingData slot.

The different features (proteins in the tan2009r1 data -above, but these could also represent peptides or MS\(^2\) spectra) are characterised by unique -names. These can be retrieved with the featureNames -function.

+above, but these could also represent peptides or +MS2^2 +spectra) are characterised by unique names. These can be retrieved with +the featureNames function.

 head(featureNames(tan2009r1))
## [1] "P20353" "P53501" "Q7KU78" "P04412" "Q7KJ73" "Q7JZN0"
@@ -814,7 +814,7 @@

Sub-cellular cluster dendrogramgetStockcol and -setStockcol) and re-used throughout pRoloc for +setStockcol) and re-used throughout pRoloc for consistent annotation.

@@ -1024,7 +1024,7 @@

Features of interest

Interactive visualisation

-

The pRolocGUI +

The pRolocGUI application allows one to explore the spatial proteomics data using an interactive, web-based interface (RStudio and Inc. 2014). The package is available from Bioconductor and can be @@ -1035,7 +1035,7 @@

Interactive visualisationlibrary("pRolocGUI") pRolocVis(tan2009r1)

-Screenshot of the pRolocGUI interface. The GUI is also available as an online app for the hyperLOPIT experiment from (Christoforou et al. 2016) at https://lgatto.shinyapps.io/christoforou2015/.
Screenshot of the pRolocGUI +Screenshot of the pRolocGUI interface. The GUI is also available as an online app for the hyperLOPIT experiment from (Christoforou et al. 2016) at https://lgatto.shinyapps.io/christoforou2015/.
Screenshot of the pRolocGUI interface. The GUI is also available as an online app for the hyperLOPIT experiment from (Christoforou et al. 2016) at https://lgatto.shinyapps.io/christoforou2015/.
@@ -1124,8 +1124,8 @@

Unsupervised MLpRoloc -also gives access to MLInterfaces’s +

Alternatively, pRoloc +also gives access to MLInterfaces’s MLean unified interface for, among others, unsupervised approaches using k-means (figure @ref(fig:plotKmeans)), hierarchical (figure @ref(fig:plotHclust)) or partitioning around medoids (figure @@ -1160,14 +1160,14 @@

Unsupervised ML

Supervised ML

-

In this section, we show how to use pRoloc to +

In this section, we show how to use pRoloc to run a typical supervised ML analysis. Several ML methods are available, including k-nearest neighbour (knn), partial least square discriminant analysis (plsda), random forest (rf), support vector machines (svm), The detailed description of each method is outside of the scope of this document. We will use support vector machines to illustrate a typical pipeline and the important points that should be paid attention to. -These points are equally valid and work, from a pRoloc +These points are equally valid and work, from a pRoloc user perspective, exactly the same for the other approaches.

Classification algorithm parameters optimisation @@ -1186,38 +1186,57 @@

Classification algorit to perform well and produce useful results, it is essential to run it with a reasonable amount of markers. In our experience, 15 such marker proteins are necessary.

-

Model accuracy is evaluated using the F1 score, \(F1 = 2 ~ \frac{precision \times recall}{precision -+ recall}\), calculated as the harmonic mean of the precision -(\(precision = \frac{tp}{tp+fp}\), a -measure of exactness – returned output is a relevant result) -and recall (\(recall=\frac{tp}{tp+fn}\), a measure of -completeness – indicating how much was missed from the output). -What we are aiming for are high generalisation accuracy, i.e high \(F1\), indicating that the marker proteins -in the test data set are consistently correctly assigned by the -algorithms.

+

Model accuracy is evaluated using the F1 score, +F1=2precision×recallprecision+recallF1 = 2 ~ +\frac{precision \times recall}{precision + recall}, +calculated as the harmonic mean of the precision +(precision=tptp+fpprecision = \frac{tp}{tp+fp}, +a measure of exactness – returned output is a relevant result) +and recall +(recall=tptp+fnrecall=\frac{tp}{tp+fn}, +a measure of completeness – indicating how much was missed from +the output). What we are aiming for are high generalisation accuracy, +i.e high +F1F1, +indicating that the marker proteins in the test data set are +consistently correctly assigned by the algorithms.

In order to evaluate how well a classifier performs on profiles it was not exposed to during its creation, we implemented the following schema. Each set of marker protein profiles, i.e. labelled with known organelle association, are separated into training and test/validation partitions by sampling 80% of the profile corresponding to each organelle (i.e. stratified) without replacement to -form the training partition \(S_{tr}\) -with the remainder becoming the test/validation partition \(S_{ts}\). The svm regularisation parameter -\(C\) and Gaussian kernel width \(sigma\) are selected using a further round -of stratified five-fold cross-validation on each training partition. All -pairs of parameters \((C_i, sigma_j)\) +form the training partition +StrS_{tr} +with the remainder becoming the test/validation partition +StsS_{ts}. +The svm regularisation parameter +CC +and Gaussian kernel width +sigmasigma +are selected using a further round of stratified five-fold +cross-validation on each training partition. All pairs of parameters +(Ci,sigmaj)(C_i, sigma_j) under consideration are assessed using the macro F1 score and the pair that produces the best performance is subsequently employed in training -a classifier on all training profiles \(S_{tr}\) prior to assessment on all -test/validation profiles \(S_{ts}\). -This procedure is repeated \(N\) times -(in the example below 10) in order to produce \(N\) macro F1 estimated generalisation -performance values (figures @ref(fig:params1) and @ref(fig:params2)). -This procedure is implemented in the svmOptimisation. See -?svmOptimisation for details, in particular the range of -\(C\) and \(sigma\) parameters and how the relevant -feature variable is defined by the fcol parameters, which -defaults to "markers".

+a classifier on all training profiles +StrS_{tr} +prior to assessment on all test/validation profiles +StsS_{ts}. +This procedure is repeated +NN +times (in the example below 10) in order to produce +NN +macro F1 estimated generalisation performance values (figures +@ref(fig:params1) and @ref(fig:params2)). This procedure is implemented +in the svmOptimisation. See ?svmOptimisation +for details, in particular the range of +CC +and +sigmasigma +parameters and how the relevant feature variable is defined by the +fcol parameters, which defaults to +"markers".

 params <- svmOptimisation(tan2009r1, fcol = "markers.orig",
               times = 100, xval = 5,
@@ -1330,7 +1349,7 @@ 

ClassificationprocessingData(svmres)

## - - - Processing information - - -
 ## Added markers from  'mrk' marker vector. Thu Jul 16 22:53:44 2015 
-## Performed svm prediction (sigma=0.1 cost=0.5) Tue Mar 12 07:15:04 2024 
+## Performed svm prediction (sigma=0.1 cost=0.5) Sun Jun 16 09:19:20 2024 
 ##  MSnbase version: 1.17.12
 tail(fvarLabels(svmres), 4)
@@ -1446,8 +1465,8 @@

Classification## Annotation: ## - - - Processing information - - - ## Added markers from 'mrk' marker vector. Thu Jul 16 22:53:44 2015 -## Performed svm prediction (sigma=0.1 cost=0.5) Tue Mar 12 07:15:04 2024 -## Added svm predictions according to thresholds: ER = 0.84, Golgi = 0.63, mitochondrion = 0.75, PM = 0.77 Tue Mar 12 07:15:04 2024 +## Performed svm prediction (sigma=0.1 cost=0.5) Sun Jun 16 09:19:20 2024 +## Added svm predictions according to thresholds: ER = 0.84, Golgi = 0.63, mitochondrion = 0.75, PM = 0.77 Sun Jun 16 09:19:21 2024 ## MSnbase version: 1.17.12

We can now visualise these results using the plotting functions presented in section @ref(sec:usml), as shown on figure @@ -1498,7 +1517,7 @@

Semi-supervised ML(Breckels et al. 2013) is an iterative method, that combines classification of proteins to known groups and detection -of new clusters. It is available in pRoloc +of new clusters. It is available in pRoloc though the phenoDisco function.

 pdres <- phenoDisco(tan2009r1, GS = 10,
@@ -1611,7 +1630,7 @@ 

Following up on novelty discoveryConclusions

This tutorial focuses on practical aspects of organelles proteomics -data analysis using pRoloc. +data analysis using pRoloc. Two important aspects have been illustrates: (1) data generation, manipulation and visualisation and (2) application of contemporary and novel machine learning techniques. Other crucial parts of a full @@ -1634,7 +1653,7 @@

Session information## R Under development (unstable) (2024-03-06 r86056) +
## R version 4.4.0 (2024-04-24)
 ## Platform: x86_64-pc-linux-gnu
 ## Running under: Ubuntu 22.04.4 LTS
 ## 
@@ -1658,102 +1677,101 @@ 

Session information## [8] base ## ## other attached packages: -## [1] class_7.3-22 pRolocdata_1.41.0 pRoloc_1.43.2 -## [4] BiocParallel_1.37.1 MLInterfaces_1.83.0 cluster_2.1.6 -## [7] annotate_1.81.2 XML_3.99-0.16.1 AnnotationDbi_1.65.2 -## [10] IRanges_2.37.1 MSnbase_2.29.3 ProtGenerics_1.35.3 -## [13] S4Vectors_0.41.4 mzR_2.37.2 Rcpp_1.0.12 -## [16] Biobase_2.63.0 BiocGenerics_0.49.1 knitr_1.45 -## [19] BiocStyle_2.31.0 +## [1] class_7.3-22 pRolocdata_1.43.0 pRoloc_1.45.1 +## [4] BiocParallel_1.39.0 MLInterfaces_1.85.0 cluster_2.1.6 +## [7] annotate_1.83.0 XML_3.99-0.16.1 AnnotationDbi_1.67.0 +## [10] IRanges_2.39.0 MSnbase_2.31.1 ProtGenerics_1.37.0 +## [13] S4Vectors_0.43.0 mzR_2.39.0 Rcpp_1.0.12 +## [16] Biobase_2.65.0 BiocGenerics_0.51.0 knitr_1.47 +## [19] BiocStyle_2.33.1 ## ## loaded via a namespace (and not attached): -## [1] splines_4.4.0 bitops_1.0-7 -## [3] filelock_1.0.3 tibble_3.2.1 -## [5] hardhat_1.3.1 preprocessCore_1.65.0 -## [7] pROC_1.18.5 rpart_4.1.23 -## [9] lifecycle_1.0.4 httr2_1.0.0 -## [11] doParallel_1.0.17 globals_0.16.3 -## [13] lattice_0.22-5 MASS_7.3-60.2 -## [15] MultiAssayExperiment_1.29.1 dendextend_1.17.1 -## [17] magrittr_2.0.3 limma_3.59.5 -## [19] plotly_4.10.4 sass_0.4.8 -## [21] rmarkdown_2.26 jquerylib_0.1.4 -## [23] yaml_2.3.8 MsCoreUtils_1.15.4 -## [25] DBI_1.2.2 RColorBrewer_1.1-3 -## [27] lubridate_1.9.3 abind_1.4-5 -## [29] zlibbioc_1.49.0 GenomicRanges_1.55.3 -## [31] purrr_1.0.2 mixtools_2.0.0 -## [33] AnnotationFilter_1.27.0 RCurl_1.98-1.14 -## [35] nnet_7.3-19 rappdirs_0.3.3 -## [37] ipred_0.9-14 lava_1.8.0 -## [39] GenomeInfoDbData_1.2.11 listenv_0.9.1 -## [41] gdata_3.0.0 parallelly_1.37.1 -## [43] pkgdown_2.0.7.9000 ncdf4_1.22 -## [45] codetools_0.2-19 DelayedArray_0.29.9 -## [47] xml2_1.3.6 tidyselect_1.2.1 -## [49] farver_2.1.1 viridis_0.6.5 -## [51] matrixStats_1.2.0 BiocFileCache_2.11.1 -## [53] jsonlite_1.8.8 caret_6.0-94 -## [55] e1071_1.7-14 survival_3.5-8 -## [57] iterators_1.0.14 systemfonts_1.0.6 -## [59] foreach_1.5.2 segmented_2.0-3 -## [61] tools_4.4.0 progress_1.2.3 -## [63] ragg_1.2.7 glue_1.7.0 -## [65] prodlim_2023.08.28 gridExtra_2.3 -## [67] SparseArray_1.3.4 xfun_0.42 -## [69] MatrixGenerics_1.15.0 GenomeInfoDb_1.39.8 -## [71] dplyr_1.1.4 withr_3.0.0 -## [73] BiocManager_1.30.22 fastmap_1.1.1 -## [75] fansi_1.0.6 digest_0.6.35 -## [77] timechange_0.3.0 R6_2.5.1 -## [79] textshaping_0.3.7 colorspace_2.1-0 -## [81] gtools_3.9.5 lpSolve_5.6.20 -## [83] biomaRt_2.59.1 RSQLite_2.3.5 -## [85] utf8_1.2.4 tidyr_1.3.1 -## [87] generics_0.1.3 hexbin_1.28.3 -## [89] data.table_1.15.2 recipes_1.0.10 -## [91] FNN_1.1.4 prettyunits_1.2.0 -## [93] PSMatch_1.7.1 httr_1.4.7 -## [95] htmlwidgets_1.6.4 S4Arrays_1.3.6 -## [97] ModelMetrics_1.2.2.2 pkgconfig_2.0.3 -## [99] gtable_0.3.4 timeDate_4032.109 -## [101] blob_1.2.4 impute_1.77.0 -## [103] XVector_0.43.1 htmltools_0.5.7 -## [105] bookdown_0.38 MALDIquant_1.22.2 -## [107] clue_0.3-65 scales_1.3.0 -## [109] png_0.1-8 gower_1.0.1 -## [111] reshape2_1.4.4 coda_0.19-4.1 -## [113] nlme_3.1-164 curl_5.2.1 -## [115] proxy_0.4-27 cachem_1.0.8 -## [117] stringr_1.5.1 parallel_4.4.0 -## [119] mzID_1.41.0 vsn_3.71.1 -## [121] desc_1.4.3 pillar_1.9.0 -## [123] grid_4.4.0 vctrs_0.6.5 -## [125] pcaMethods_1.95.0 randomForest_4.7-1.1 -## [127] dbplyr_2.4.0 xtable_1.8-4 -## [129] evaluate_0.23 mvtnorm_1.2-4 -## [131] cli_3.6.2 compiler_4.4.0 -## [133] rlang_1.1.3 crayon_1.5.2 -## [135] future.apply_1.11.1 labeling_0.4.3 -## [137] LaplacesDemon_16.1.6 mclust_6.1 -## [139] QFeatures_1.13.2 affy_1.81.0 -## [141] plyr_1.8.9 fs_1.6.3 -## [143] stringi_1.8.3 viridisLite_0.4.2 -## [145] munsell_0.5.0 Biostrings_2.71.3 -## [147] lazyeval_0.2.2 Matrix_1.6-5 -## [149] hms_1.1.3 future_1.33.1 -## [151] bit64_4.0.5 ggplot2_3.5.0 -## [153] KEGGREST_1.43.0 statmod_1.5.0 -## [155] highr_0.10 SummarizedExperiment_1.33.3 -## [157] kernlab_0.9-32 igraph_2.0.2 -## [159] memoise_2.0.1 affyio_1.73.0 -## [161] bslib_0.6.1 sampling_2.10 -## [163] bit_4.0.5

+## [1] splines_4.4.0 filelock_1.0.3 +## [3] tibble_3.2.1 hardhat_1.4.0 +## [5] preprocessCore_1.67.0 pROC_1.18.5 +## [7] rpart_4.1.23 lifecycle_1.0.4 +## [9] httr2_1.0.1 doParallel_1.0.17 +## [11] globals_0.16.3 lattice_0.22-6 +## [13] MASS_7.3-60.2 MultiAssayExperiment_1.31.3 +## [15] dendextend_1.17.1 magrittr_2.0.3 +## [17] limma_3.61.2 plotly_4.10.4 +## [19] sass_0.4.9 rmarkdown_2.27 +## [21] jquerylib_0.1.4 yaml_2.3.8 +## [23] MsCoreUtils_1.17.0 DBI_1.2.3 +## [25] RColorBrewer_1.1-3 lubridate_1.9.3 +## [27] abind_1.4-5 zlibbioc_1.51.1 +## [29] GenomicRanges_1.57.1 purrr_1.0.2 +## [31] mixtools_2.0.0 AnnotationFilter_1.29.0 +## [33] nnet_7.3-19 rappdirs_0.3.3 +## [35] ipred_0.9-14 lava_1.8.0 +## [37] GenomeInfoDbData_1.2.12 listenv_0.9.1 +## [39] gdata_3.0.0 parallelly_1.37.1 +## [41] pkgdown_2.0.9.9000 ncdf4_1.22 +## [43] codetools_0.2-20 DelayedArray_0.31.1 +## [45] xml2_1.3.6 tidyselect_1.2.1 +## [47] farver_2.1.2 UCSC.utils_1.1.0 +## [49] viridis_0.6.5 matrixStats_1.3.0 +## [51] BiocFileCache_2.13.0 jsonlite_1.8.8 +## [53] caret_6.0-94 e1071_1.7-14 +## [55] survival_3.7-0 iterators_1.0.14 +## [57] systemfonts_1.1.0 foreach_1.5.2 +## [59] segmented_2.1-0 tools_4.4.0 +## [61] progress_1.2.3 ragg_1.3.2 +## [63] glue_1.7.0 prodlim_2023.08.28 +## [65] gridExtra_2.3 SparseArray_1.5.8 +## [67] xfun_0.44 MatrixGenerics_1.17.0 +## [69] GenomeInfoDb_1.41.1 dplyr_1.1.4 +## [71] withr_3.0.0 BiocManager_1.30.23 +## [73] fastmap_1.2.0 fansi_1.0.6 +## [75] digest_0.6.35 timechange_0.3.0 +## [77] R6_2.5.1 textshaping_0.4.0 +## [79] colorspace_2.1-0 gtools_3.9.5 +## [81] lpSolve_5.6.20 biomaRt_2.61.1 +## [83] RSQLite_2.3.7 utf8_1.2.4 +## [85] tidyr_1.3.1 generics_0.1.3 +## [87] hexbin_1.28.3 data.table_1.15.4 +## [89] recipes_1.0.10 FNN_1.1.4 +## [91] prettyunits_1.2.0 PSMatch_1.9.0 +## [93] httr_1.4.7 htmlwidgets_1.6.4 +## [95] S4Arrays_1.5.1 ModelMetrics_1.2.2.2 +## [97] pkgconfig_2.0.3 gtable_0.3.5 +## [99] timeDate_4032.109 blob_1.2.4 +## [101] impute_1.79.0 XVector_0.45.0 +## [103] htmltools_0.5.8.1 bookdown_0.39 +## [105] MALDIquant_1.22.2 clue_0.3-65 +## [107] scales_1.3.0 png_0.1-8 +## [109] gower_1.0.1 reshape2_1.4.4 +## [111] coda_0.19-4.1 nlme_3.1-165 +## [113] curl_5.2.1 proxy_0.4-27 +## [115] cachem_1.1.0 stringr_1.5.1 +## [117] parallel_4.4.0 mzID_1.43.0 +## [119] vsn_3.73.0 desc_1.4.3 +## [121] pillar_1.9.0 grid_4.4.0 +## [123] vctrs_0.6.5 pcaMethods_1.97.0 +## [125] randomForest_4.7-1.1 dbplyr_2.5.0 +## [127] xtable_1.8-4 evaluate_0.24.0 +## [129] mvtnorm_1.2-5 cli_3.6.2 +## [131] compiler_4.4.0 rlang_1.1.4 +## [133] crayon_1.5.2 future.apply_1.11.2 +## [135] labeling_0.4.3 LaplacesDemon_16.1.6 +## [137] mclust_6.1.1 QFeatures_1.15.1 +## [139] affy_1.83.0 plyr_1.8.9 +## [141] fs_1.6.4 stringi_1.8.4 +## [143] viridisLite_0.4.2 munsell_0.5.1 +## [145] Biostrings_2.73.1 lazyeval_0.2.2 +## [147] Matrix_1.7-0 hms_1.1.3 +## [149] bit64_4.0.5 future_1.33.2 +## [151] ggplot2_3.5.1 KEGGREST_1.45.0 +## [153] statmod_1.5.0 highr_0.11 +## [155] SummarizedExperiment_1.35.0 kernlab_0.9-32 +## [157] igraph_2.0.3 memoise_2.0.1 +## [159] affyio_1.75.0 bslib_0.7.0 +## [161] sampling_2.10 bit_4.0.5

References

-
+
Breckels, Lisa M, Laurent Gatto, Andy Christoforou, Arnoud J Groen, Kathryn S Lilley, and Matthew W B Trotter. 2013. “The Effect of @@ -1840,7 +1858,7 @@

References↩︎

  • Briefly, the itraqdata raw iTRAQ4-plex data -is quantified by trapezoidation using MSnbase +is quantified by trapezoidation using MSnbase functionality. See the MSnbase-demo vignette for details.↩︎

  • https://lvdmaaten.github.io/tsne/↩︎

  • http://distill.pub/2016/misread-tsne/↩︎

  • @@ -1850,9 +1868,7 @@

    References - -

    +
    @@ -1865,16 +1881,16 @@

    References

    -

    Site built with pkgdown 2.0.7.9000.

    +

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      @@ -45,7 +44,7 @@
      - +
      @@ -95,7 +94,7 @@

      Machine learning techniques available in

      Laurent Gatto

      - de Duve Institute, UCLouvain, Belgium
      Source: vignettes/v02-pRoloc-ml.Rmd + de Duve Institute, UCLouvain, Belgium
      Source: vignettes/v02-pRoloc-ml.Rmd
      @@ -125,7 +124,7 @@

      Laurent

      Introduction

      -

      For a general practical introduction to pRoloc, +

      For a general practical introduction to pRoloc, readers are referred to the tutorial, available using vignette("pRoloc-tutorial", package = "pRoloc"). The following document provides a overview of the algorithms available in @@ -137,16 +136,16 @@

      Introduction

      Data sets

      -

      We provide 144 test data sets in the pRolocdata -package that can be readily used with pRoloc. +

      We provide 144 test data sets in the pRolocdata +package that can be readily used with pRoloc. The data set can be listed with pRolocdata and loaded with the data function. Each data set, including its origin, is individually documented.

      The data sets are distributed as MSnSet instances. Briefly, these are dedicated containers for quantitation data as well as feature and sample meta-data. More details about MSnSets are available -in the pRoloc -tutorial and in the MSnbase +in the pRoloc +tutorial and in the MSnbase package, that defined the class.

       library("pRolocdata")
      @@ -175,14 +174,14 @@ 

      Other omics data

      While our primary biological domain is quantitative proteomics, with special emphasis on spatial proteomics, the underlying class -infrastructure on which pRoloc and -implemented in the Bioconductor MSnbase +infrastructure on which pRoloc and +implemented in the Bioconductor MSnbase package enables the conversion from/to transcriptomics data, in particular microarray data available as ExpressionSet objects using the as coercion methods (see the MSnSet section in the MSnbase-development vignette). As a result, it is straightforward to apply the methods summarised here in detailed in the -other pRoloc +other pRoloc vignettes to these other data structures.

      @@ -193,18 +192,18 @@

      Unsupervised machine learningMLInterfaces +the plot2D function and the MLInterfaces package (Carey et al., n.d.). The former takes an MSnSet instance and represents the data on a scatter plot along the first two principal components. Arbitrary feature meta-data can be represented using different colours and point characters. The reader is referred to the manual page available through ?plot2D for more details and examples.

      -

      pRoloc +

      pRoloc also implements a MLean method for MSnSet instances, allowing to use the relevant infrastructure with the organelle proteomics framework. Although provides a common interface to -unsupervised and numerous supervised algorithms, we refer to the pRoloc +unsupervised and numerous supervised algorithms, we refer to the pRoloc tutorial for its usage to several clustering algorithms.

      Note Current development efforts in terms of clustering are described on the Clustering infrastructure wiki @@ -229,7 +228,7 @@

      Supervised machine learningAlgorithms used

      k-nearest neighbour (KNN) Function knn from -package class. For +package class. For each row of the test set, the k nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote over the k classes, with ties broken at random. @@ -248,7 +247,7 @@

      Algorithms usedpRoloc +

      pRoloc makes use of the functions svm from package and ksvm from .

      Artificial neural network (ANN) Function @@ -262,18 +261,27 @@

      Algorithms used\(\chi^2\)) Assignment based on -squared differences between a labelled marker and a new feature to be -classified. Canonical protein correlation profile method (PCP) uses -squared differences between a labelled marker and new features. In (Andersen et al. 2003), \(\chi^2\) is defined as , i.e. \(\chi^{2} = \frac{\sum_{i=1}^{n} (x_i - -m_i)^{2}}{n}\), whereas (Wiese et al. -2007) divide by the value the squared value by the value of the -reference feature in each fraction, i.e. \(\chi^{2} = \sum_{i=1}^{n}\frac{(x_i - -m_i)^{2}}{m_i}\), where \(x_i\) +

      Chi-square +(χ2\chi^2) +Assignment based on squared differences between a labelled marker and a +new feature to be classified. Canonical protein correlation profile +method (PCP) uses squared differences between a labelled marker and new +features. In (Andersen et al. 2003), +χ2\chi^2 +is defined as , +i.e. χ2=i=1n(ximi)2n\chi^{2} = +\frac{\sum_{i=1}^{n} (x_i - m_i)^{2}}{n}, +whereas (Wiese et al. 2007) divide by the +value the squared value by the value of the reference feature in each +fraction, +i.e. χ2=i=1n(ximi)2mi\chi^{2} = \sum_{i=1}^{n}\frac{(x_i - +m_i)^{2}}{m_i}, where +xix_i is normalised intensity of feature x in fraction i, -\(m_i\) is the normalised intensity of -marker m in fraction i and n is the number of -fractions available. We will use the former definition.

      +mim_i +is the normalised intensity of marker m in fraction i +and n is the number of fractions available. We will use the +former definition.

      PerTurbo From (Courty, Burger, and Laurent 2011): PerTurbo, an original, non-parametric and efficient classification method is presented here. In our framework, @@ -286,7 +294,7 @@

      Algorithms usedpRoloc +

      The PerTurbo implementation comes from the pRoloc packages.

      @@ -296,32 +304,37 @@

      Estimating algorithm parameterssigma of the Radial Basis Function (Gaussian kernel) -\(exp(-\sigma \| x - x' \|^2 )\) +exp(σxx2)exp(-\sigma \| x - x' \|^2 ) and the cost (slack) parameter (controlling the tolerance to mis-classification) of our SVM classifier. The number of neighbours k of the KNN classifier is equally important as will be discussed in this sections.

      The next figure illustrates the -effect of different choices of \(k\) +effect of different choices of +kk using organelle proteomics data from (Dunkley et -al. 2006) (dunkley2006 from pRolocdata). -As highlighted in the squared region, we can see that using a low \(k\) (k = 1 on the left) will -result in very specific classification boundaries that precisely follow -the contour or our marker set as opposed to a higher number of -neighbours (k = 8 on the right). While one could be tempted to -believe that optimised classification boundaries are -preferable, it is essential to remember that these boundaries are -specific to the marker set used to construct them, while there is -absolutely no reason to expect these regions to faithfully separate any -new data points, in particular proteins that we wish to classify to an -organelle. In other words, the highly specific k = 1 -classification boundaries are over-fitted for the marker set -or, in other words, lack generalisation to new instances. We will -demonstrate this using simulated data taken from (James et al. 2013) and show what detrimental +al. 2006) (dunkley2006 from pRolocdata). +As highlighted in the squared region, we can see that using a low +kk +(k = 1 on the left) will result in very specific classification +boundaries that precisely follow the contour or our marker set as +opposed to a higher number of neighbours (k = 8 on the right). +While one could be tempted to believe that optimised +classification boundaries are preferable, it is essential to remember +that these boundaries are specific to the marker set used to construct +them, while there is absolutely no reason to expect these regions to +faithfully separate any new data points, in particular proteins that we +wish to classify to an organelle. In other words, the highly specific +k = 1 classification boundaries are over-fitted for +the marker set or, in other words, lack generalisation to new instances. +We will demonstrate this using simulated data taken from (James et al. 2013) and show what detrimental effect over-fitting has on new data.

      -Classification boundaries using k=1 or k=8 on the dunkley2006 data.
      Classification boundaries using \(k=1\) or \(k=8\) on the dunkley2006 -data.
      +Classification boundaries using k=1 or k=8 on the dunkley2006 data.
      Classification boundaries using +k=1k=1 +or +k=8k=8 +on the dunkley2006 data.

      The figure below uses 2 x 100 simulated data points belonging to either of the orange or blue classes. @@ -339,11 +352,13 @@

      Estimating algorithm parameters
      -The KNN classifier using k = 1 (left, solid classification boundaries) and k = 100 (right, solid classification boundaries) compared the Bayes decision boundaries (see original material for details). Reproduced with permission from (James et al. 2013).
      The KNN classifier using \(k = 1\) (left, solid classification -boundaries) and \(k = 100\) (right, -solid classification boundaries) compared the Bayes decision boundaries -(see original material for details). Reproduced with permission from -(James et al. 2013).
      +The KNN classifier using k = 1 (left, solid classification boundaries) and k = 100 (right, solid classification boundaries) compared the Bayes decision boundaries (see original material for details). Reproduced with permission from (James et al. 2013).
      The KNN classifier using +k=1k = 1 +(left, solid classification boundaries) and +k=100k = 100 +(right, solid classification boundaries) compared the Bayes decision +boundaries (see original material for details). Reproduced with +permission from (James et al. 2013).

      To quantify the effect of flexibility and lack thereof in defining the classification boundaries, (James et al. @@ -370,10 +385,11 @@

      Estimating algorithm parameters
      Effect of train and test error with respect to model complexity. The former decreases for lower values of k while the test error reaches a minimum around k = 10 before increasing again. Reproduced with permission from (James et al. 2013).
      Effect of train and test error with respect to -model complexity. The former decreases for lower values of \(k\) while the test error reaches a minimum -around \(k = 10\) before increasing -again. Reproduced with permission from (James et -al. 2013).
      +model complexity. The former decreases for lower values of +kk +while the test error reaches a minimum around +k=10k = 10 +before increasing again. Reproduced with permission from (James et al. 2013).

      These results show that adequate optimisation of the model parameters are essential to avoid either too flexible models (that do not @@ -389,7 +405,7 @@

      Estimating algorithm parameters

      Default analysis scheme

      -

      Below, we present a typical classification analysis using pRoloc. +

      Below, we present a typical classification analysis using pRoloc. The analysis typically consists of two steps. The first one is to optimise the classifier parameters to be used for training and testing (see above). A range of parameters are tested using the labelled data, @@ -412,15 +428,19 @@

      Parameter optimisationn = 100 times to sample n accuracy metrics (see below) values using n, possibly different, optimised parameters for evaluation.

      -

      Models accuracy is evaluated using the F1 score, \(F1 = 2 ~ \frac{precision \times recall}{precision -+ recall}\), calculated as the harmonic mean of the precision -(\(precision = \frac{tp}{tp+fp}\), a -measure of exactness – returned output is a relevant result) -and recall (\(recall=\frac{tp}{tp+fn}\), a measure of -completeness – indicating how much was missed from the output). -What we are aiming for are high generalisation accuracy, i.e high \(F1\), indicating that the marker proteins -in the test data set are consistently correctly assigned by the -algorithms.

      +

      Models accuracy is evaluated using the F1 score, +F1=2precision×recallprecision+recallF1 = 2 ~ \frac{precision \times recall}{precision + recall}, +calculated as the harmonic mean of the precision +(precision=tptp+fpprecision = +\frac{tp}{tp+fp}, a measure of +exactness – returned output is a relevant result) and recall +(recall=tptp+fnrecall=\frac{tp}{tp+fn}, +a measure of completeness – indicating how much was missed from +the output). What we are aiming for are high generalisation accuracy, +i.e high +F1F1, +indicating that the marker proteins in the test data set are +consistently correctly assigned by the algorithms.

      The results of the optimisation procedure are stored in an GenRegRes object that can be inspected, plotted and best parameter pairs can be extracted.

      @@ -445,9 +465,9 @@

      Parameter optimisation## Results ## macro F1: ## Min. 1st Qu. Median Mean 3rd Qu. Max. -## 0.7755 0.8479 0.8606 0.8569 0.8873 0.9354 -## best sigma: 1 0.1 -## best cost: 8 4 2 16 1 +## 0.7804 0.8085 0.8211 0.8412 0.8842 0.9385 +## best sigma: 0.1 1 +## best cost: 8 16 2 1 ## Use getWarnings() to see warnings.

      @@ -570,13 +590,13 @@

      Comparison of different classifiers poster1, we show that different classification algorithms provide very similar performance. We have extended this comparison on various datasets -distributed in the pRolocdata +distributed in the pRolocdata package. On figure @ref{fig:f1box}, we illustrate how different algorithms reach very similar performances on most of our test datasets.

      Comparison of classification performances of several contemporary classification algorithms on data from the pRolocdata package.
      Comparison of classification performances of -several contemporary classification algorithms on data from the pRolocdata +several contemporary classification algorithms on data from the pRolocdata package.

      @@ -605,16 +625,26 @@

      Semi-supervised machine learning(Lisa M. Breckels et al. 2013) (figure below). It uses the labelled -(i.e. markers, noted \(D_L\)) and -unlabelled (i.e. proteins of unknown localisation, noted \(D_U\)) sets of the input data. The -algorithm is repeated \(N\) times (the -times argument in the phenoDisco function). At -each iteration, each organelle class \(D_{L}^{i}\) and the unlabelled complement -are clustered using Gaussian mixture modelling. While unlabelled members -that systematically cluster with \(D_{L}^{i}\) and pass outlier detection are -labelled as new putative members of class \(i\), any example of \(D_U\) which are not merged with any any of -the \(D_{L}^{i}\) and are consistently -clustered together throughout the \(N\) +(i.e. markers, noted +DLD_L) +and unlabelled (i.e. proteins of unknown localisation, noted +DUD_U) +sets of the input data. The algorithm is repeated +NN +times (the times argument in the phenoDisco +function). At each iteration, each organelle class +DLiD_{L}^{i} +and the unlabelled complement are clustered using Gaussian mixture +modelling. While unlabelled members that systematically cluster with +DLiD_{L}^{i} +and pass outlier detection are labelled as new putative members of class +ii, +any example of +DUD_U +which are not merged with any any of the +DLiD_{L}^{i} +and are consistently clustered together throughout the +NN iterations are considered members of a new phenotype.

      The PhenoDisco iterative algorithm.
      The PhenoDisco iterative algorithm.
      @@ -652,7 +682,7 @@

      Session information## R Under development (unstable) (2024-03-06 r86056) +
      ## R version 4.4.0 (2024-04-24)
       ## Platform: x86_64-pc-linux-gnu
       ## Running under: Ubuntu 22.04.4 LTS
       ## 
      @@ -676,100 +706,99 @@ 

      Session information## [8] base ## ## other attached packages: -## [1] pRolocdata_1.41.0 pRoloc_1.43.2 BiocParallel_1.37.1 -## [4] MLInterfaces_1.83.0 cluster_2.1.6 annotate_1.81.2 -## [7] XML_3.99-0.16.1 AnnotationDbi_1.65.2 IRanges_2.37.1 -## [10] MSnbase_2.29.3 ProtGenerics_1.35.3 S4Vectors_0.41.4 -## [13] mzR_2.37.2 Rcpp_1.0.12 Biobase_2.63.0 -## [16] BiocGenerics_0.49.1 knitr_1.45 BiocStyle_2.31.0 +## [1] pRolocdata_1.43.0 pRoloc_1.45.1 BiocParallel_1.39.0 +## [4] MLInterfaces_1.85.0 cluster_2.1.6 annotate_1.83.0 +## [7] XML_3.99-0.16.1 AnnotationDbi_1.67.0 IRanges_2.39.0 +## [10] MSnbase_2.31.1 ProtGenerics_1.37.0 S4Vectors_0.43.0 +## [13] mzR_2.39.0 Rcpp_1.0.12 Biobase_2.65.0 +## [16] BiocGenerics_0.51.0 knitr_1.47 BiocStyle_2.33.1 ## ## loaded via a namespace (and not attached): -## [1] splines_4.4.0 bitops_1.0-7 -## [3] filelock_1.0.3 tibble_3.2.1 -## [5] hardhat_1.3.1 preprocessCore_1.65.0 -## [7] pROC_1.18.5 rpart_4.1.23 -## [9] lifecycle_1.0.4 httr2_1.0.0 -## [11] doParallel_1.0.17 globals_0.16.3 -## [13] lattice_0.22-5 MASS_7.3-60.2 -## [15] MultiAssayExperiment_1.29.1 dendextend_1.17.1 -## [17] magrittr_2.0.3 limma_3.59.5 -## [19] plotly_4.10.4 sass_0.4.8 -## [21] rmarkdown_2.26 jquerylib_0.1.4 -## [23] yaml_2.3.8 MsCoreUtils_1.15.4 -## [25] DBI_1.2.2 RColorBrewer_1.1-3 -## [27] lubridate_1.9.3 abind_1.4-5 -## [29] zlibbioc_1.49.0 GenomicRanges_1.55.3 -## [31] purrr_1.0.2 mixtools_2.0.0 -## [33] AnnotationFilter_1.27.0 RCurl_1.98-1.14 -## [35] nnet_7.3-19 rappdirs_0.3.3 -## [37] ipred_0.9-14 lava_1.8.0 -## [39] GenomeInfoDbData_1.2.11 listenv_0.9.1 -## [41] gdata_3.0.0 parallelly_1.37.1 -## [43] pkgdown_2.0.7.9000 ncdf4_1.22 -## [45] codetools_0.2-19 DelayedArray_0.29.9 -## [47] xml2_1.3.6 tidyselect_1.2.1 -## [49] viridis_0.6.5 matrixStats_1.2.0 -## [51] BiocFileCache_2.11.1 jsonlite_1.8.8 -## [53] caret_6.0-94 e1071_1.7-14 -## [55] survival_3.5-8 iterators_1.0.14 -## [57] systemfonts_1.0.6 foreach_1.5.2 -## [59] segmented_2.0-3 tools_4.4.0 -## [61] progress_1.2.3 ragg_1.2.7 -## [63] glue_1.7.0 prodlim_2023.08.28 -## [65] gridExtra_2.3 SparseArray_1.3.4 -## [67] xfun_0.42 MatrixGenerics_1.15.0 -## [69] GenomeInfoDb_1.39.8 dplyr_1.1.4 -## [71] withr_3.0.0 BiocManager_1.30.22 -## [73] fastmap_1.1.1 fansi_1.0.6 -## [75] digest_0.6.35 timechange_0.3.0 -## [77] R6_2.5.1 textshaping_0.3.7 -## [79] colorspace_2.1-0 gtools_3.9.5 -## [81] lpSolve_5.6.20 biomaRt_2.59.1 -## [83] RSQLite_2.3.5 utf8_1.2.4 -## [85] tidyr_1.3.1 generics_0.1.3 -## [87] hexbin_1.28.3 data.table_1.15.2 -## [89] recipes_1.0.10 FNN_1.1.4 -## [91] class_7.3-22 prettyunits_1.2.0 -## [93] PSMatch_1.7.1 httr_1.4.7 -## [95] htmlwidgets_1.6.4 S4Arrays_1.3.6 -## [97] ModelMetrics_1.2.2.2 pkgconfig_2.0.3 -## [99] gtable_0.3.4 timeDate_4032.109 -## [101] blob_1.2.4 impute_1.77.0 -## [103] XVector_0.43.1 htmltools_0.5.7 -## [105] bookdown_0.38 MALDIquant_1.22.2 -## [107] clue_0.3-65 scales_1.3.0 -## [109] png_0.1-8 gower_1.0.1 -## [111] reshape2_1.4.4 coda_0.19-4.1 -## [113] nlme_3.1-164 curl_5.2.1 -## [115] proxy_0.4-27 cachem_1.0.8 -## [117] stringr_1.5.1 parallel_4.4.0 -## [119] mzID_1.41.0 vsn_3.71.1 -## [121] desc_1.4.3 pillar_1.9.0 -## [123] grid_4.4.0 vctrs_0.6.5 -## [125] pcaMethods_1.95.0 randomForest_4.7-1.1 -## [127] dbplyr_2.4.0 xtable_1.8-4 -## [129] evaluate_0.23 mvtnorm_1.2-4 -## [131] cli_3.6.2 compiler_4.4.0 -## [133] rlang_1.1.3 crayon_1.5.2 -## [135] future.apply_1.11.1 LaplacesDemon_16.1.6 -## [137] mclust_6.1 QFeatures_1.13.2 -## [139] affy_1.81.0 plyr_1.8.9 -## [141] fs_1.6.3 stringi_1.8.3 -## [143] viridisLite_0.4.2 munsell_0.5.0 -## [145] Biostrings_2.71.3 lazyeval_0.2.2 -## [147] Matrix_1.6-5 hms_1.1.3 -## [149] future_1.33.1 bit64_4.0.5 -## [151] ggplot2_3.5.0 KEGGREST_1.43.0 -## [153] statmod_1.5.0 SummarizedExperiment_1.33.3 -## [155] kernlab_0.9-32 igraph_2.0.2 -## [157] memoise_2.0.1 affyio_1.73.0 -## [159] bslib_0.6.1 sampling_2.10 -## [161] bit_4.0.5

      +## [1] splines_4.4.0 filelock_1.0.3 +## [3] tibble_3.2.1 hardhat_1.4.0 +## [5] preprocessCore_1.67.0 pROC_1.18.5 +## [7] rpart_4.1.23 lifecycle_1.0.4 +## [9] httr2_1.0.1 doParallel_1.0.17 +## [11] globals_0.16.3 lattice_0.22-6 +## [13] MASS_7.3-60.2 MultiAssayExperiment_1.31.3 +## [15] dendextend_1.17.1 magrittr_2.0.3 +## [17] limma_3.61.2 plotly_4.10.4 +## [19] sass_0.4.9 rmarkdown_2.27 +## [21] jquerylib_0.1.4 yaml_2.3.8 +## [23] MsCoreUtils_1.17.0 DBI_1.2.3 +## [25] RColorBrewer_1.1-3 lubridate_1.9.3 +## [27] abind_1.4-5 zlibbioc_1.51.1 +## [29] GenomicRanges_1.57.1 purrr_1.0.2 +## [31] mixtools_2.0.0 AnnotationFilter_1.29.0 +## [33] nnet_7.3-19 rappdirs_0.3.3 +## [35] ipred_0.9-14 lava_1.8.0 +## [37] GenomeInfoDbData_1.2.12 listenv_0.9.1 +## [39] gdata_3.0.0 parallelly_1.37.1 +## [41] pkgdown_2.0.9.9000 ncdf4_1.22 +## [43] codetools_0.2-20 DelayedArray_0.31.1 +## [45] xml2_1.3.6 tidyselect_1.2.1 +## [47] UCSC.utils_1.1.0 viridis_0.6.5 +## [49] matrixStats_1.3.0 BiocFileCache_2.13.0 +## [51] jsonlite_1.8.8 caret_6.0-94 +## [53] e1071_1.7-14 survival_3.7-0 +## [55] iterators_1.0.14 systemfonts_1.1.0 +## [57] foreach_1.5.2 segmented_2.1-0 +## [59] tools_4.4.0 progress_1.2.3 +## [61] ragg_1.3.2 glue_1.7.0 +## [63] prodlim_2023.08.28 gridExtra_2.3 +## [65] SparseArray_1.5.8 xfun_0.44 +## [67] MatrixGenerics_1.17.0 GenomeInfoDb_1.41.1 +## [69] dplyr_1.1.4 withr_3.0.0 +## [71] BiocManager_1.30.23 fastmap_1.2.0 +## [73] fansi_1.0.6 digest_0.6.35 +## [75] timechange_0.3.0 R6_2.5.1 +## [77] textshaping_0.4.0 colorspace_2.1-0 +## [79] gtools_3.9.5 lpSolve_5.6.20 +## [81] biomaRt_2.61.1 RSQLite_2.3.7 +## [83] utf8_1.2.4 tidyr_1.3.1 +## [85] generics_0.1.3 hexbin_1.28.3 +## [87] data.table_1.15.4 recipes_1.0.10 +## [89] FNN_1.1.4 class_7.3-22 +## [91] prettyunits_1.2.0 PSMatch_1.9.0 +## [93] httr_1.4.7 htmlwidgets_1.6.4 +## [95] S4Arrays_1.5.1 ModelMetrics_1.2.2.2 +## [97] pkgconfig_2.0.3 gtable_0.3.5 +## [99] timeDate_4032.109 blob_1.2.4 +## [101] impute_1.79.0 XVector_0.45.0 +## [103] htmltools_0.5.8.1 bookdown_0.39 +## [105] MALDIquant_1.22.2 clue_0.3-65 +## [107] scales_1.3.0 png_0.1-8 +## [109] gower_1.0.1 reshape2_1.4.4 +## [111] coda_0.19-4.1 nlme_3.1-165 +## [113] curl_5.2.1 proxy_0.4-27 +## [115] cachem_1.1.0 stringr_1.5.1 +## [117] parallel_4.4.0 mzID_1.43.0 +## [119] vsn_3.73.0 desc_1.4.3 +## [121] pillar_1.9.0 grid_4.4.0 +## [123] vctrs_0.6.5 pcaMethods_1.97.0 +## [125] randomForest_4.7-1.1 dbplyr_2.5.0 +## [127] xtable_1.8-4 evaluate_0.24.0 +## [129] mvtnorm_1.2-5 cli_3.6.2 +## [131] compiler_4.4.0 rlang_1.1.4 +## [133] crayon_1.5.2 future.apply_1.11.2 +## [135] LaplacesDemon_16.1.6 mclust_6.1.1 +## [137] QFeatures_1.15.1 affy_1.83.0 +## [139] plyr_1.8.9 fs_1.6.4 +## [141] stringi_1.8.4 viridisLite_0.4.2 +## [143] munsell_0.5.1 Biostrings_2.73.1 +## [145] lazyeval_0.2.2 Matrix_1.7-0 +## [147] hms_1.1.3 bit64_4.0.5 +## [149] future_1.33.2 ggplot2_3.5.1 +## [151] KEGGREST_1.45.0 statmod_1.5.0 +## [153] SummarizedExperiment_1.35.0 kernlab_0.9-32 +## [155] igraph_2.0.3 memoise_2.0.1 +## [157] affyio_1.75.0 bslib_0.7.0 +## [159] sampling_2.10 bit_4.0.5

      References

      -
      +
      Andersen, Jens S., Christopher J. Wilkinson, Thibault Mayor, Peter Mortensen, Erich A. Nigg, and Matthias Mann. 2003. “Proteomic @@ -866,9 +895,7 @@

      References - -

      +
      @@ -881,16 +908,16 @@

      References

      -

      Site built with pkgdown 2.0.7.9000.

      +

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    - - + + diff --git a/articles/v03-pRoloc-bayesian.html b/articles/v03-pRoloc-bayesian.html index 079ed20a..83896b24 100644 --- a/articles/v03-pRoloc-bayesian.html +++ b/articles/v03-pRoloc-bayesian.html @@ -6,20 +6,19 @@ Bayesian Analysis of Spatial Proteomics data using pRoloc • pRoloc - + - - +
    @@ -45,7 +44,7 @@
    - +
    @@ -98,7 +97,7 @@

    Laurent Gatto

    - Source: vignettes/v03-pRoloc-bayesian.Rmd + Source: vignettes/v03-pRoloc-bayesian.Rmd
    @@ -147,16 +146,16 @@

    Laurent

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/articles/v04-pRoloc-goannotations.html b/articles/v04-pRoloc-goannotations.html index a9ef5de7..417217e3 100644 --- a/articles/v04-pRoloc-goannotations.html +++ b/articles/v04-pRoloc-goannotations.html @@ -6,20 +6,19 @@ Annotating spatial proteomics data • pRoloc - + - - +
    @@ -45,7 +44,7 @@
    - +
    @@ -98,7 +97,7 @@

    Lisa M. UK

    Laurent Gatto

    - de Duve Institute, UCLouvain, Belgium
    Source: vignettes/v04-pRoloc-goannotations.Rmd + de Duve Institute, UCLouvain, Belgium
    Source: vignettes/v04-pRoloc-goannotations.Rmd
    @@ -166,7 +165,7 @@

    IntroductionpRolocGUI +and explored using the pRolocGUI package.

    In this vignette we present a step-by-step guide showing users how to (1) how to add protein annotations, here we use the GO database as an @@ -180,7 +179,7 @@

    Loading the dataWe will demonstrate our pipeline for adding and ranking annotation information using a LOPIT experiment on Pluripotent Mouse Embryonic stems (Christoforou -et al 2016), available and documented in the pRolocdata +et al 2016), available and documented in the pRolocdata data package as hyperlopit2015.

     library("pRoloc")
    @@ -195,15 +194,19 @@ 

    Adding sub-cellular locali

    All GO terms associated to proteins that appear in the dataset are retrieved and used to create a binary matrix where a 1 (0) at position -\((i,j)\) indicates that term \(j\) has (not) been used to annotate feature -\(i\). This matrix is appended and -stored in the feature data slot of the MSnSet dataset using -the addGoAnnotations function. We first however need to -prepare annotation parameters that will enable us to query the Biomart -repository using the package, from where we are able to retrieve GO -terms. The specific Biomart repository and query will depend on the -species under study and the type of features. This can be set using the -setAnnotationParams function.

    +(i,j)(i,j) +indicates that term +jj +has (not) been used to annotate feature +ii. +This matrix is appended and stored in the feature data slot of the +MSnSet dataset using the addGoAnnotations +function. We first however need to prepare annotation parameters that +will enable us to query the Biomart repository using the package, from +where we are able to retrieve GO terms. The specific Biomart repository +and query will depend on the species under study and the type of +features. This can be set using the setAnnotationParams +function.

    In the code chunk below we set the annotation parameters for the hyperLOPIT2015 dataset. As this species used was mouse and the featureNames of the hyperLOPIT2015 dataset @@ -320,7 +323,7 @@

    Examining distancesdd[[1]]

    ## Object of class "ClustDist"
     ## fcol =  GOAnnotations 
    -##  term =  GO:0005856 
    +##  term =  cytoskeleton 
     ##  id =  cytoskeleton 
     ##  nrow =  32 
     ## k's tested: 1 2 3 
    @@ -370,9 +373,7 @@ 

    Examining distances - -

    +
    @@ -385,16 +386,16 @@

    Examining distances

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    - +
    @@ -45,7 +44,7 @@
    @@ -99,7 +98,7 @@

    Lisa M. UK

    Laurent Gatto

    - de Duve Institute, UCLouvain, Belgium
    Source: vignettes/v05-pRoloc-transfer-learning.Rmd + de Duve Institute, UCLouvain, Belgium
    Source: vignettes/v05-pRoloc-transfer-learning.Rmd
    @@ -160,7 +159,7 @@

    IntroductionpRoloc +localisation of proteins from the pRoloc package, as described in

    Breckels LM, Holden S, Wonjar D, Mulvey CM, Christoforou A, Groen A, @@ -169,10 +168,11 @@

    Introduction10.1371/journal.pcbi.1004920.

    Two algorithms were developed: a transfer learning algorithm based on -the \(k\)-nearest neighbour classifier, -coined kNN-TL hereafter, described in section @ref(sec:knntl), and one -based on the support vector machine algorithm, termed SVM-TL, described -in section @ref(sec:svmtl).

    +the +kk-nearest +neighbour classifier, coined kNN-TL hereafter, described in section +@ref(sec:knntl), and one based on the support vector machine algorithm, +termed SVM-TL, described in section @ref(sec:svmtl).

    @@ -184,14 +184,18 @@

    The Gene Ontology\((i,j)\) indicates that term \(j\) has (not) been used to annotate feature -\(i\).

    +create a binary matrix where a one (zero) at position +(i,j)(i,j) +indicates that term +jj +has (not) been used to annotate feature +ii.

    The GO terms are retrieved from an appropriate repository using the -biomaRt +biomaRt package. The specific Biomart repository and query will depend on the species under study and the type of features. The first step is to prepare annotation parameters that will enable to perform the query. The -pRoloc +pRoloc package provides a dedicated infrastructure to set up the query to the annotation resource and prepare the GO data for subsequent analyses. This infrastructure is composed of:

    @@ -203,7 +207,7 @@

    The Gene Ontology(Lisa -M. Breckels et al. 2013), available and documented in the pRolocdata +M. Breckels et al. 2013), available and documented in the pRolocdata experiment package as andy2011.

    ## MSnSet (storageMode: lockedEnvironment)
    -## assayData: 1371 features, 914 samples 
    +## assayData: 1371 features, 913 samples 
     ##   element names: exprs 
     ## protocolData: none
     ## phenoData: none
    @@ -280,8 +284,8 @@ 

    Preparing the auxiliary data from the GO ontology## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - -## Constructed GO set using cellular_component namespace [Tue Mar 12 07:18:53 2024] -## MSnbase version: 2.29.3

    +## Constructed GO set using cellular_component namespace [Sun Jun 16 09:21:34 2024] +## MSnbase version: 2.31.1
     exprs(andygoset)[1:7, 1:4]
    ##        GO:0016020 GO:0005783 GO:0005789 GO:0005730
    @@ -294,7 +298,7 @@ 

    Preparing the auxiliary data from the GO ontology## Q96SQ9 1 1 1 0

    We now have a primary data set, composed of 1371 protein quantitative profiles for 8 fractions along the density gradient and an auxiliary -data set for 914 cellular compartment GO terms for the same 1371 +data set for 913 cellular compartment GO terms for the same 1371 features.

    @@ -308,7 +312,7 @@

    A note on reproducibilityGO.db +such as specific organism annotations and the GO.db package to use specific versioned (and thus traceable) annotations.

    @@ -346,7 +350,7 @@

    The Human Protein Atlas
     ## HPA data
     library("hpar")
    -
    ## This is hpar version 1.45.0,
    +
    ## This is hpar version 1.47.0,
     ## based on the Human Protein Atlas
     ##   Version: 21.1
     ##   Release data: 2022.05.31
    @@ -424,14 +428,14 @@ 

    Protein-protein interactionsPSICQUIC +
  • The PSICQUIC package provides an R interfaces to the HUPO Proteomics Standard Initiative (HUPO-PSI) project, which standardises programmatic access to molecular interaction databases. This approach enables to query great many resources in one go but, as noted in the vignettes, for bulk interactions, it is recommended to directly download databases from individual PSICQUIC providers.

  • -
  • The STRINGdb +

  • The STRINGdb package provides a direct interface to the STRING protein-protein interactions database. This package can be used to generate a table as the one used below. The exact procedure is described in the @@ -444,7 +448,7 @@

    Protein-protein interactionspRolocdata +distributed with the pRolocdata package.

     ppif <- system.file("extdata/tabdelimited._gHentss2F9k.txt.gz", package = "pRolocdata")
    @@ -519,7 +523,7 @@ 

    Protein-protein interactionsSupport vector machine transfer learning

    The SVM-TL method descibed in (L. M. Breckels -et al. 2016) has not yet been incorporated in the pRoloc +et al. 2016) has not yet been incorporated in the pRoloc package. The code implementing the method is currently available in its own repository:

    https://github.com/ComputationalProteomicsUnit/lpsvm-tl-code

    @@ -564,7 +568,7 @@

    Optimal weights\(number~of~classes^{number~of~weights}\) +numberofclassesnumberofweightsnumber~of~classes^{number~of~weights} possibilities; see below). One would generally defined more weights (for example 0, 0.25, 0.5, 0.75, 1 or 0, 0.33, 0.67, 1) to explore more fine-grained integration opportunities. The possible weight combinations @@ -625,8 +629,15 @@

    Optimal weights\(k_P\) and \(k_A\) values. Here, based on -knnOptimisation, we use 3 and 3, for \(k_P\) and \(k_A\) respectively.

    +the best +kPk_P +and +kAk_A +values. Here, based on knnOptimisation, we use 3 and 3, for +kPk_P +and +kAk_A +respectively.

    Finally, to assess the validity of the weight selection, it should be repeated a certain number of times (default value is 50). As the weight optimisation can become very time consuming for a wide range of weights @@ -645,7 +656,7 @@

    Optimal weightsBiocParallel +to the BiocParallel package; an appropriate backend will be defined automatically according to the underlying architecture and user-defined backends can be defined through the BPPARAM argument2. Also, in the interest @@ -658,7 +669,7 @@

    Optimal weights= c(3, 3), fcol = "markers.tl", times = 5)

    -
    ## Removing 498 columns with only 0s.
    +
    ## Removing 497 columns with only 0s.
    ## Note: vector will be ordered according to classes: Cytosol Cytosol/Nucleus ER Golgi Lysosome Mitochondrion Nucleus PM Ribosome 40S Ribosome 60S (as names are not explicitly defined)
     topt
    @@ -674,17 +685,17 @@

    Optimal weights## Results ## macro F1: ## Min. 1st Qu. Median Mean 3rd Qu. Max. -## 0.8101 0.8361 0.8396 0.8561 0.8899 0.9046 +## 0.8096 0.8396 0.8740 0.8630 0.8871 0.9046 ## best theta: ## Cytosol Cytosol.Nucleus ER Golgi Lysosome Mitochondrion Nucleus PM -## weight:0 0 0 3 2 0 2 1 2 -## weight:0.33 2 2 0 0 0 1 0 2 -## weight:0.67 1 3 0 3 2 0 2 0 -## weight:1 2 0 2 0 3 2 2 1 +## weight:0 0 0 4 4 0 1 0 1 +## weight:0.33 1 4 0 0 0 0 0 4 +## weight:0.67 0 1 0 1 1 0 1 0 +## weight:1 4 0 1 0 4 4 4 0 ## Ribosome.40S Ribosome.60S -## weight:0 5 2 -## weight:0.33 0 2 -## weight:0.67 0 1 +## weight:0 5 1 +## weight:0.33 0 4 +## weight:0.67 0 0 ## weight:1 0 0

  • The optimisation is performed on the labelled marker examples only. When removing unlabelled non-marker proteins (the @@ -724,9 +735,9 @@

    Choosing weights
     getParams(topt)
    ##         Cytosol Cytosol/Nucleus              ER           Golgi        Lysosome 
    -##       0.6666667       0.6666667       0.0000000       0.6666667       1.0000000 
    +##       1.0000000       0.3333333       0.0000000       0.0000000       1.0000000 
     ##   Mitochondrion         Nucleus              PM    Ribosome 40S    Ribosome 60S 
    -##       0.3333333       0.6666667       0.3333333       0.0000000       0.3333333
    +## 1.0000000 1.0000000 0.3333333 0.0000000 0.3333333

    We provide the best parameters for the extensive parameter optimisation search, as provided by getParams:

    @@ -762,13 +773,13 @@ 

    Applying best theta weightsandy2011 <- getPredictions(andy2011, fcol = "knntl")

    ## ans
     ## Chromatin associated              Cytosol      Cytosol/Nucleus 
    -##                   11                  275                   45 
    +##                   11                  299                   43 
     ##             Endosome                   ER                Golgi 
    -##                   12                  199                   75 
    +##                   12                  196                   72 
     ##             Lysosome        Mitochondrion              Nucleus 
    -##                   63                  257                  113 
    +##                   60                  254                  108 
     ##                   PM         Ribosome 40S         Ribosome 60S 
    -##                  248                   19                   54
    +## 245 19 52
     setStockcol(paste0(getStockcol(), "80"))
     ptsze <- exp(fData(andy2011)$knntl.scores) - 1
    @@ -792,22 +803,23 @@ 

    Applying best theta weights

    Conclusions

    -

    This vignette describes the application of a weighted \(k\)-nearest neighbour transfer learning -algorithm and its application to the sub-cellular localisation -prediction of proteins using quantitative proteomics data as primary -data and Gene Ontology-derived binary data as auxiliary data source. The -algorithm can be used with various data sources (we show how to compile -binary data from the Human Protein Atlas in section @ref(sec:hpaaux)) -and have successfully applied the algorithm (L. -M. Breckels et al. 2016) on third-party quantitative auxiliary -data.

    +

    This vignette describes the application of a weighted +kk-nearest +neighbour transfer learning algorithm and its application to the +sub-cellular localisation prediction of proteins using quantitative +proteomics data as primary data and Gene Ontology-derived binary data as +auxiliary data source. The algorithm can be used with various data +sources (we show how to compile binary data from the Human Protein Atlas +in section @ref(sec:hpaaux)) and have successfully applied the algorithm +(L. M. Breckels et al. 2016) on +third-party quantitative auxiliary data.

    Session information

    All software and respective versions used to produce this document are listed below.

    -
    ## R Under development (unstable) (2024-03-06 r86056)
    +
    ## R version 4.4.0 (2024-04-24)
     ## Platform: x86_64-pc-linux-gnu
     ## Running under: Ubuntu 22.04.4 LTS
     ## 
    @@ -831,102 +843,101 @@ 

    Session information## [8] base ## ## other attached packages: -## [1] hpar_1.45.0 biomaRt_2.59.1 class_7.3-22 -## [4] pRolocdata_1.41.0 pRoloc_1.43.2 BiocParallel_1.37.1 -## [7] MLInterfaces_1.83.0 cluster_2.1.6 annotate_1.81.2 -## [10] XML_3.99-0.16.1 AnnotationDbi_1.65.2 IRanges_2.37.1 -## [13] MSnbase_2.29.3 ProtGenerics_1.35.3 S4Vectors_0.41.4 -## [16] mzR_2.37.2 Rcpp_1.0.12 Biobase_2.63.0 -## [19] BiocGenerics_0.49.1 knitr_1.45 BiocStyle_2.31.0 +## [1] hpar_1.47.0 biomaRt_2.61.1 class_7.3-22 +## [4] pRolocdata_1.43.0 pRoloc_1.45.1 BiocParallel_1.39.0 +## [7] MLInterfaces_1.85.0 cluster_2.1.6 annotate_1.83.0 +## [10] XML_3.99-0.16.1 AnnotationDbi_1.67.0 IRanges_2.39.0 +## [13] MSnbase_2.31.1 ProtGenerics_1.37.0 S4Vectors_0.43.0 +## [16] mzR_2.39.0 Rcpp_1.0.12 Biobase_2.65.0 +## [19] BiocGenerics_0.51.0 knitr_1.47 BiocStyle_2.33.1 ## ## loaded via a namespace (and not attached): -## [1] splines_4.4.0 bitops_1.0-7 -## [3] filelock_1.0.3 tibble_3.2.1 -## [5] hardhat_1.3.1 preprocessCore_1.65.0 -## [7] pROC_1.18.5 rpart_4.1.23 -## [9] lifecycle_1.0.4 httr2_1.0.0 -## [11] doParallel_1.0.17 globals_0.16.3 -## [13] lattice_0.22-5 MASS_7.3-60.2 -## [15] MultiAssayExperiment_1.29.1 dendextend_1.17.1 -## [17] magrittr_2.0.3 limma_3.59.5 -## [19] plotly_4.10.4 sass_0.4.8 -## [21] rmarkdown_2.26 jquerylib_0.1.4 -## [23] yaml_2.3.8 MsCoreUtils_1.15.4 -## [25] DBI_1.2.2 RColorBrewer_1.1-3 -## [27] lubridate_1.9.3 abind_1.4-5 -## [29] zlibbioc_1.49.0 GenomicRanges_1.55.3 -## [31] purrr_1.0.2 mixtools_2.0.0 -## [33] AnnotationFilter_1.27.0 RCurl_1.98-1.14 -## [35] nnet_7.3-19 rappdirs_0.3.3 -## [37] ipred_0.9-14 lava_1.8.0 -## [39] GenomeInfoDbData_1.2.11 listenv_0.9.1 -## [41] parallelly_1.37.1 pkgdown_2.0.7.9000 -## [43] ncdf4_1.22 codetools_0.2-19 -## [45] DelayedArray_0.29.9 xml2_1.3.6 -## [47] tidyselect_1.2.1 viridis_0.6.5 -## [49] matrixStats_1.2.0 BiocFileCache_2.11.1 -## [51] jsonlite_1.8.8 caret_6.0-94 -## [53] e1071_1.7-14 survival_3.5-8 -## [55] iterators_1.0.14 systemfonts_1.0.6 -## [57] foreach_1.5.2 segmented_2.0-3 -## [59] tools_4.4.0 progress_1.2.3 -## [61] ragg_1.2.7 glue_1.7.0 -## [63] prodlim_2023.08.28 gridExtra_2.3 -## [65] SparseArray_1.3.4 xfun_0.42 -## [67] MatrixGenerics_1.15.0 GenomeInfoDb_1.39.8 -## [69] dplyr_1.1.4 withr_3.0.0 -## [71] BiocManager_1.30.22 fastmap_1.1.1 -## [73] fansi_1.0.6 digest_0.6.35 -## [75] mime_0.12 timechange_0.3.0 -## [77] R6_2.5.1 textshaping_0.3.7 -## [79] colorspace_2.1-0 gtools_3.9.5 -## [81] lpSolve_5.6.20 RSQLite_2.3.5 -## [83] utf8_1.2.4 tidyr_1.3.1 -## [85] generics_0.1.3 hexbin_1.28.3 -## [87] data.table_1.15.2 recipes_1.0.10 -## [89] FNN_1.1.4 prettyunits_1.2.0 -## [91] PSMatch_1.7.1 httr_1.4.7 -## [93] htmlwidgets_1.6.4 S4Arrays_1.3.6 -## [95] ModelMetrics_1.2.2.2 pkgconfig_2.0.3 -## [97] gtable_0.3.4 timeDate_4032.109 -## [99] blob_1.2.4 impute_1.77.0 -## [101] XVector_0.43.1 htmltools_0.5.7 -## [103] bookdown_0.38 MALDIquant_1.22.2 -## [105] clue_0.3-65 scales_1.3.0 -## [107] png_0.1-8 gower_1.0.1 -## [109] reshape2_1.4.4 coda_0.19-4.1 -## [111] nlme_3.1-164 curl_5.2.1 -## [113] proxy_0.4-27 cachem_1.0.8 -## [115] stringr_1.5.1 BiocVersion_3.19.1 -## [117] parallel_4.4.0 mzID_1.41.0 -## [119] vsn_3.71.1 desc_1.4.3 -## [121] pillar_1.9.0 grid_4.4.0 -## [123] vctrs_0.6.5 pcaMethods_1.95.0 -## [125] randomForest_4.7-1.1 dbplyr_2.4.0 -## [127] xtable_1.8-4 evaluate_0.23 -## [129] mvtnorm_1.2-4 cli_3.6.2 -## [131] compiler_4.4.0 rlang_1.1.3 -## [133] crayon_1.5.2 future.apply_1.11.1 -## [135] LaplacesDemon_16.1.6 mclust_6.1 -## [137] QFeatures_1.13.2 affy_1.81.0 -## [139] plyr_1.8.9 fs_1.6.3 -## [141] stringi_1.8.3 viridisLite_0.4.2 -## [143] munsell_0.5.0 Biostrings_2.71.3 -## [145] lazyeval_0.2.2 Matrix_1.6-5 -## [147] ExperimentHub_2.11.1 hms_1.1.3 -## [149] future_1.33.1 bit64_4.0.5 -## [151] ggplot2_3.5.0 KEGGREST_1.43.0 -## [153] statmod_1.5.0 highr_0.10 -## [155] AnnotationHub_3.11.1 SummarizedExperiment_1.33.3 -## [157] kernlab_0.9-32 igraph_2.0.2 -## [159] memoise_2.0.1 affyio_1.73.0 -## [161] bslib_0.6.1 sampling_2.10 -## [163] bit_4.0.5

    +## [1] splines_4.4.0 filelock_1.0.3 +## [3] tibble_3.2.1 hardhat_1.4.0 +## [5] preprocessCore_1.67.0 pROC_1.18.5 +## [7] rpart_4.1.23 lifecycle_1.0.4 +## [9] httr2_1.0.1 doParallel_1.0.17 +## [11] globals_0.16.3 lattice_0.22-6 +## [13] MASS_7.3-60.2 MultiAssayExperiment_1.31.3 +## [15] dendextend_1.17.1 magrittr_2.0.3 +## [17] limma_3.61.2 plotly_4.10.4 +## [19] sass_0.4.9 rmarkdown_2.27 +## [21] jquerylib_0.1.4 yaml_2.3.8 +## [23] MsCoreUtils_1.17.0 DBI_1.2.3 +## [25] RColorBrewer_1.1-3 lubridate_1.9.3 +## [27] abind_1.4-5 zlibbioc_1.51.1 +## [29] GenomicRanges_1.57.1 purrr_1.0.2 +## [31] mixtools_2.0.0 AnnotationFilter_1.29.0 +## [33] nnet_7.3-19 rappdirs_0.3.3 +## [35] ipred_0.9-14 lava_1.8.0 +## [37] GenomeInfoDbData_1.2.12 listenv_0.9.1 +## [39] parallelly_1.37.1 pkgdown_2.0.9.9000 +## [41] ncdf4_1.22 codetools_0.2-20 +## [43] DelayedArray_0.31.1 xml2_1.3.6 +## [45] tidyselect_1.2.1 UCSC.utils_1.1.0 +## [47] viridis_0.6.5 matrixStats_1.3.0 +## [49] BiocFileCache_2.13.0 jsonlite_1.8.8 +## [51] caret_6.0-94 e1071_1.7-14 +## [53] survival_3.7-0 iterators_1.0.14 +## [55] systemfonts_1.1.0 foreach_1.5.2 +## [57] segmented_2.1-0 tools_4.4.0 +## [59] progress_1.2.3 ragg_1.3.2 +## [61] glue_1.7.0 prodlim_2023.08.28 +## [63] gridExtra_2.3 SparseArray_1.5.8 +## [65] xfun_0.44 MatrixGenerics_1.17.0 +## [67] GenomeInfoDb_1.41.1 dplyr_1.1.4 +## [69] withr_3.0.0 BiocManager_1.30.23 +## [71] fastmap_1.2.0 fansi_1.0.6 +## [73] digest_0.6.35 mime_0.12 +## [75] timechange_0.3.0 R6_2.5.1 +## [77] textshaping_0.4.0 colorspace_2.1-0 +## [79] gtools_3.9.5 lpSolve_5.6.20 +## [81] RSQLite_2.3.7 utf8_1.2.4 +## [83] tidyr_1.3.1 generics_0.1.3 +## [85] hexbin_1.28.3 data.table_1.15.4 +## [87] recipes_1.0.10 FNN_1.1.4 +## [89] prettyunits_1.2.0 PSMatch_1.9.0 +## [91] httr_1.4.7 htmlwidgets_1.6.4 +## [93] S4Arrays_1.5.1 ModelMetrics_1.2.2.2 +## [95] pkgconfig_2.0.3 gtable_0.3.5 +## [97] timeDate_4032.109 blob_1.2.4 +## [99] impute_1.79.0 XVector_0.45.0 +## [101] htmltools_0.5.8.1 bookdown_0.39 +## [103] MALDIquant_1.22.2 clue_0.3-65 +## [105] scales_1.3.0 png_0.1-8 +## [107] gower_1.0.1 reshape2_1.4.4 +## [109] coda_0.19-4.1 nlme_3.1-165 +## [111] curl_5.2.1 proxy_0.4-27 +## [113] cachem_1.1.0 stringr_1.5.1 +## [115] BiocVersion_3.20.0 parallel_4.4.0 +## [117] mzID_1.43.0 vsn_3.73.0 +## [119] desc_1.4.3 pillar_1.9.0 +## [121] grid_4.4.0 vctrs_0.6.5 +## [123] pcaMethods_1.97.0 randomForest_4.7-1.1 +## [125] dbplyr_2.5.0 xtable_1.8-4 +## [127] evaluate_0.24.0 mvtnorm_1.2-5 +## [129] cli_3.6.2 compiler_4.4.0 +## [131] rlang_1.1.4 crayon_1.5.2 +## [133] future.apply_1.11.2 LaplacesDemon_16.1.6 +## [135] mclust_6.1.1 QFeatures_1.15.1 +## [137] affy_1.83.0 plyr_1.8.9 +## [139] fs_1.6.4 stringi_1.8.4 +## [141] viridisLite_0.4.2 munsell_0.5.1 +## [143] Biostrings_2.73.1 lazyeval_0.2.2 +## [145] Matrix_1.7-0 ExperimentHub_2.13.0 +## [147] hms_1.1.3 bit64_4.0.5 +## [149] future_1.33.2 ggplot2_3.5.1 +## [151] KEGGREST_1.45.0 statmod_1.5.0 +## [153] highr_0.11 AnnotationHub_3.13.0 +## [155] SummarizedExperiment_1.35.0 kernlab_0.9-32 +## [157] igraph_2.0.3 memoise_2.0.1 +## [159] affyio_1.75.0 bslib_0.7.0 +## [161] sampling_2.10 bit_4.0.5

    References

    -
    +
    Ashburner, M, C A Ball, J A Blake, D Botstein, H Butler, J M Cherry, A P Davis, et al. 2000. “Gene Ontology: Tool for the Unification of @@ -981,9 +992,7 @@

    References - -

    +
    @@ -996,16 +1005,16 @@

    References

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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Citation • pRoloc - +

    @@ -28,7 +28,7 @@
    - +
    @@ -69,32 +69,32 @@

    Authors and Citation

    - +
    • -

      Laurent Gatto. Author, maintainer. +

      Laurent Gatto. Author, maintainer.

    • -

      Lisa Breckels. Author. +

      Lisa Breckels. Author.

    • -

      Thomas Burger. Contributor. +

      Thomas Burger. Contributor.

    • -

      Samuel Wieczorek. Contributor. +

      Samuel Wieczorek. Contributor.

    • -

      Oliver Crook. Author. +

      Oliver Crook. Author.

    Citation

    - Source: inst/CITATION + Source: inst/CITATION
    @@ -153,15 +153,15 @@

    Citation

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/index.html b/index.html index 2fcc49fa..c39a4428 100644 --- a/index.html +++ b/index.html @@ -6,7 +6,7 @@ A unifying bioinformatics framework for spatial proteomics • pRoloc - + @@ -19,7 +19,7 @@ - +
    @@ -45,7 +45,7 @@
    - +
    @@ -218,16 +218,16 @@

    Dev status

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/news/index.html b/news/index.html index fc6d0518..a9da1be1 100644 --- a/news/index.html +++ b/news/index.html @@ -1,9 +1,9 @@ -Changelog • pRolocChangelog • pRoloc - +
    @@ -28,7 +28,7 @@
    - +

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    +
    + +
    +

    Changes in version 1.45.1

    +
    • Import mclust::estep().
    • +
    +
    +

    Changes in version 1.45.0

    +
    • New devel version
    • +
    +
    @@ -376,15 +387,15 @@

    Changes in version 1.17.0
    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/pkgdown.yml b/pkgdown.yml index fbc635ac..6b5a1e6d 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -1,11 +1,10 @@ -pandoc: 3.1.1 -pkgdown: 2.0.7.9000 -pkgdown_sha: 1924ebf3b65bc037c9ece195ecd7869f1d203e94 +pandoc: '3.2' +pkgdown: 2.0.9.9000 +pkgdown_sha: c0a09226e29533e62d67c18bf14406ee5466d5d2 articles: v01-pRoloc-tutorial: v01-pRoloc-tutorial.html v02-pRoloc-ml: v02-pRoloc-ml.html v03-pRoloc-bayesian: v03-pRoloc-bayesian.html v04-pRoloc-goannotations: v04-pRoloc-goannotations.html v05-pRoloc-transfer-learning: v05-pRoloc-transfer-learning.html -last_built: 2024-03-12T07:12Z - +last_built: 2024-06-16T09:17Z diff --git a/reference/AnnotationParams-class.html b/reference/AnnotationParams-class.html index cb5733e9..d60209f4 100644 --- a/reference/AnnotationParams-class.html +++ b/reference/AnnotationParams-class.html @@ -1,12 +1,12 @@ -Class "AnnotationParams" — AnnotationParams-class • pRolocClass "AnnotationParams" — AnnotationParams-class • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    @@ -83,7 +83,7 @@

    Class "AnnotationParams"

    Objects from the Class

    - +

    Objects can be created and set with the setAnnotationParams function. Object are created by calling without any arguments @@ -153,7 +153,7 @@

    Slots

    with the version of the AnnotationParams class of the current instance.

    - +

    Methods

    @@ -161,7 +161,7 @@

    Methods

    signature(object = "AnnotationParams"): to display objects.

    - +

    Author

    @@ -209,7 +209,7 @@

    Examples

    #> Using the 'ENSEMBL_MART_ENSEMBL' BioMart database #> Using the 'hsapiens_gene_ensembl' dataset #> Using 'uniprotswissprot' as filter -#> Created on Tue Mar 12 07:12:59 2024 +#> Created on Sun Jun 16 09:17:16 2024
    @@ -224,15 +224,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

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    @@ -32,7 +32,7 @@
    - +
    -

    The ClustDist summaries algorithm information, from - running the clustDist function, such as the number - of k's tested for the kmeans, and mean and normalised - pairwise (Euclidean) distances per numer of component +

    The ClustDist summaries algorithm information, from + running the clustDist function, such as the number + of k's tested for the kmeans, and mean and normalised + pairwise (Euclidean) distances per numer of component clusters tested.

    @@ -95,7 +95,7 @@

    Slots

    the number of k clusters tested.

    dist:
    -

    Object of class "list" storing +

    Object of class "list" storing the list of distance matrices.

    term:
    @@ -123,7 +123,7 @@

    Slots

    the feature column name in the corresponding MSnSet where the protein set information is stored.

    - +

    Methods

    @@ -133,7 +133,7 @@

    Methods

    show

    Shows the object.

    - +

    Author

    @@ -155,7 +155,7 @@

    Examples

    library('pRolocdata') #> -#> This is pRolocdata version 1.41.0. +#> This is pRolocdata version 1.43.0. #> Use 'pRolocdata()' to list available data sets. data(dunkley2006) par <- setAnnotationParams(inputs = @@ -176,7 +176,7 @@

    Examples

    ## filter xx <- filterMinMarkers(xx, n = 50) -#> Retaining 18 out of 165 in GOAnnotations +#> Retaining 18 out of 166 in GOAnnotations xx <- filterMaxMarkers(xx, p = .25) #> Retaining 14 out of 18 in GOAnnotations @@ -206,15 +206,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    @@ -29,7 +29,7 @@
    - +
    @@ -99,7 +99,7 @@

    Slots

    The version of the instance. For development purposes only.

    - +

    Methods

    @@ -131,7 +131,7 @@

    Methods

    Plots a boxplot of the distance results per protein set.

    - +

    Author

    @@ -158,7 +158,7 @@

    Examples

    ## filter xx <- filterMinMarkers(xx, n = 50) -#> Retaining 18 out of 165 in GOAnnotations +#> Retaining 18 out of 166 in GOAnnotations xx <- filterMaxMarkers(xx, p = .25) #> Retaining 14 out of 18 in GOAnnotations @@ -217,15 +217,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/GenRegRes-class.html b/reference/GenRegRes-class.html index 26fa3fec..6955c95c 100644 --- a/reference/GenRegRes-class.html +++ b/reference/GenRegRes-class.html @@ -1,9 +1,9 @@ -Class "GenRegRes" and "ThetaRegRes" — GenRegRes-class • pRolocClass "GenRegRes" and "ThetaRegRes" — GenRegRes-class • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -122,23 +122,23 @@

    Slots

    testPartitions:

    Object of class "list" with respective times test partitions.

    - +
    datasize:

    Object of class "list" with details about the respective inner and outter training and testing data sizes.

    - +

    Only in ThetaRegRes:

    predictions:

    A list of predictions for the optimisation iterations.

    - +
    otherWeights:

    Alternative best theta weigts: a vector per iterations, NULL if no other best weights were found.

    - - + +

    Methods

    @@ -172,7 +172,7 @@

    Methods

    (i.e. heigh weights). See favourPrimary below.

    - +
    getSeed

    Returns the seed used for the optimisation run.

    @@ -191,12 +191,12 @@

    Methods

    show

    Shows the object.

    - +

    Other functions

    - - + +

    Only for ThetaRegRes:

    combineThetaRegRes(object)

    Takes a list of @@ -214,7 +214,7 @@

    Other functions

    when multiple best theta weights are available.

    - +

    Author

    @@ -267,15 +267,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/MCMCParams.html b/reference/MCMCParams.html index 710d732c..77f13268 100644 --- a/reference/MCMCParams.html +++ b/reference/MCMCParams.html @@ -1,11 +1,11 @@ -Instrastructure to store and process MCMC results — MCMCChains-class • pRolocInstrastructure to store and process MCMC results — MCMCChains-class • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -79,58 +79,60 @@

    Instrastructure to store and process MCMC results

    -
    chains(object)
    -
    -# S4 method for MCMCParams
    -show(object)
    -
    -# S4 method for ComponentParam
    -show(object)
    -
    -# S4 method for MCMCChain
    -show(object)
    -
    -# S4 method for MCMCChains
    -length(x)
    -
    -# S4 method for MCMCParams
    -length(x)
    -
    -# S4 method for MCMCChains,ANY,ANY
    -[[(x, i, j = "missing", drop = "missing")
    -
    -# S4 method for MCMCParams,ANY,ANY
    -[[(x, i, j = "missing", drop = "missing")
    -
    -# S4 method for MCMCChains,ANY,ANY,ANY
    -[(x, i, j = "missing", drop = "missing")
    -
    -# S4 method for MCMCParams,ANY,ANY,ANY
    -[(x, i, j = "missing", drop = "missing")
    -
    -# S4 method for MCMCChains
    -show(object)
    +
    chains(object)
    +
    +# S4 method for class 'MCMCParams'
    +show(object)
    +
    +# S4 method for class 'ComponentParam'
    +show(object)
    +
    +# S4 method for class 'MCMCChain'
    +show(object)
    +
    +# S4 method for class 'MCMCChains'
    +length(x)
    +
    +# S4 method for class 'MCMCParams'
    +length(x)
    +
    +# S4 method for class 'MCMCChains,ANY,ANY'
    +x[[i, j = "missing", drop = "missing"]]
    +
    +# S4 method for class 'MCMCParams,ANY,ANY'
    +x[[i, j = "missing", drop = "missing"]]
    +
    +# S4 method for class 'MCMCChains,ANY,ANY,ANY'
    +x[i, j = "missing", drop = "missing"]
    +
    +# S4 method for class 'MCMCParams,ANY,ANY,ANY'
    +x[i, j = "missing", drop = "missing"]
    +
    +# S4 method for class 'MCMCChains'
    +show(object)

    Arguments

    -
    object
    + + +
    object

    An instance of appropriate class.

    -
    x
    +
    x

    Object to be subset.

    -
    i
    +
    i

    An integer(). Should be of length 1 for [[.

    -
    j
    +
    j

    Missing.

    -
    drop
    +
    drop

    Missing.

    @@ -147,7 +149,7 @@

    Details

    Slots

    - +
    chains

    list() containing the individual full MCMC chain @@ -245,15 +247,15 @@

    See also

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/MLearn-methods.html b/reference/MLearn-methods.html index 8e50d22a..3fe437f7 100644 --- a/reference/MLearn-methods.html +++ b/reference/MLearn-methods.html @@ -1,11 +1,11 @@ -The MLearn interface for machine learning — MLearn-methods • pRolocThe MLearn interface for machine learning — MLearn-methods • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -87,7 +87,7 @@

    Methods

    the .method schema on the MSnSet data input using the trainInd numeric indices as train data.

    - +
    signature(formula = "formula", data = "MSnSet", .method = "learnerSchema", trainInd = "xvalSpec")

    In this case, an instance of xvalSpec is used for @@ -100,8 +100,8 @@

    Methods

    partitioning around medoids (pamI) clustering algorithms using MLInterface's MLearn interface.

    - - + +

    See also

    @@ -120,15 +120,15 @@

    See also

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/MartInstance-class.html b/reference/MartInstance-class.html index 28c80b4c..cfae4144 100644 --- a/reference/MartInstance-class.html +++ b/reference/MartInstance-class.html @@ -1,12 +1,12 @@ -Class "MartInstance" — MartInstance-class • pRolocClass "MartInstance" — MartInstance-class • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    - - + +

    Internal infrastructure to query/handle several individual mart instance. See MartInterface.R for details.

    @@ -98,15 +98,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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e0927c9c..14551395 100644 --- a/reference/QSep-class.html +++ b/reference/QSep-class.html @@ -1,5 +1,5 @@ -Quantify resolution of a spatial proteomics experiment — QSep-class • pRolocQuantify resolution of a spatial proteomics experiment — QSep-class • pRoloc - +

    @@ -65,7 +65,7 @@
    - +
    - +

    The QSep infrastructure provide a way to quantify the resolution of a spatial proteomics experiment, i.e. to quantify how @@ -137,7 +137,7 @@

    Quantify resolution of a spatial proteomics experiment

    Objects from the Class

    - +

    Objects can be created by calls using the constructor QSep (see below).

    @@ -167,7 +167,7 @@

    Slots

    storing the class version of the object.

    - +

    Extends

    @@ -222,7 +222,7 @@

    Methods and functions

    defines whether normalised distances should be plotted.

    - +

    References

    @@ -535,15 +535,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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z<-Ys+OKIOXX;D?^#!UIgkq9vBpF#=dn$y-N_9)pvWvBZEt&cA zIPcsm98OZtE&u!e7bUT{1M2J~>Vs4XGM>lN+;S~@%PVAjlT4HrTJvmEd#_DY@fWT# zZNX{>>zN^-Bd1N|EwgxC;%(D)UUk1rnvlE;RXdXd%cWP6x_7>v=R2~$AN+yo`>GQ? zj?FfQ*YR3XDu~WC<>pPUiuUq1D5hcGGMi;MJY^lbF20B8#u!||wq%ZdrbHvdtW&j9 z2vN6h{bq3Q@q62cGdx`GF7Ee(xCYi)(WBrqMw)<{wL`ie{A8^&4#&CAZOV|_lHi2J z!odL#NCznKPp7fVRVpw%P4<2eZuhmMshQtckL?KP+^csygo*CUlmz-Ae|=i-i-&fo&)ZZ+>Q#GBJNM;v5oBgzaXL>gS35$NUPeW_O42mw{{e0tex(2a diff --git a/reference/SpatProtVis-class.html b/reference/SpatProtVis-class.html index f923788c..dc663404 100644 --- a/reference/SpatProtVis-class.html +++ b/reference/SpatProtVis-class.html @@ -1,5 +1,5 @@ -Class SpatProtVis — SpatProtVis-class • pRolocClass SpatProtVis — SpatProtVis-class • pRoloc - +

    @@ -36,7 +36,7 @@
    - +
    - +

    A class for spatial proteomics visualisation, that upon instantiation, pre-computes all defined visualisations. Objects can be created with @@ -96,29 +96,32 @@

    Class SpatProtVis

    Arguments

    -
    x
    + + +

    +
    x

    An instance of class MSnSet to visualise.

    -
    methods
    +
    methods

    Dimensionality reduction methods to be used to visualise the data. Must be contained in plot2Dmethods (except "scree"). See plot2D for details.

    - -
    dims
    + +
    dims

    A list of numerics defining dimensions used for plotting. Default are 1 and 2. If provided, the length of this list must be identical to the length of methods.

    - -
    methargs
    + +
    methargs

    A list of additional arguments to be passed for each visualisation method. If provided, the length of this list must be identical to the length of methods.

    - -
    ...
    + +
    ...

    Additional arguments. Currently ignored.

    @@ -128,7 +131,7 @@

    Slots

    A "list" of matrices containing the feature projections in 2 dimensions.

    - +
    data:

    The original spatial proteomics data stored as an "MSnSet".

    @@ -144,8 +147,8 @@

    Slots

    dataset. By default, this is set using the variable name used at object creation.

    - - + +

    Methods

    @@ -159,8 +162,8 @@

    Methods

    show:

    A simple textual summary of the object.

    - - + +

    Author

    @@ -237,15 +240,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/addGoAnnotations.html b/reference/addGoAnnotations.html index 0a1d67f2..d5d34589 100644 --- a/reference/addGoAnnotations.html +++ b/reference/addGoAnnotations.html @@ -1,9 +1,9 @@ -Add GO annotations — addGoAnnotations • pRolocAdd GO annotations — addGoAnnotations • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -87,39 +87,39 @@

    Add GO annotations

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    params
    -

    An instance of class AnnotationParams. +

    params
    +

    An instance of class AnnotationParams. If missing, getAnnotationParams will be used.

    -
    evidence
    +
    evidence

    GO evidence filtering.

    -
    useID
    +
    useID

    Logical. Should GO term names or identifiers be used? -If TRUE, identifiers will be used. If FALSE GO term +If TRUE, identifiers will be used. If FALSE GO term names will be used.

    -
    fcol
    -

    Character. Name of the matrix of annotations to be added to the +

    fcol
    +

    Character. Name of the matrix of annotations to be added to the fData default is GOAnnotations

    -
    ...
    +
    ...

    Other arguments passed to makeGoSet

    Value

    - - -

    An updated MSnSet with new feature data column +

    An updated MSnSet with new feature data column called GOAnnotations containing a matrix of GO annotations

    @@ -145,11 +145,11 @@

    Examples

    ## add protein sets/annotation information xx <- addGoAnnotations(dunkley2006, par) dim(fData(xx)$GOAnnotations) -#> [1] 689 165 +#> [1] 689 166 ## filter sets xx <- filterMinMarkers(xx, n = 50) -#> Retaining 18 out of 165 in GOAnnotations +#> Retaining 18 out of 166 in GOAnnotations dim(fData(xx)$GOAnnotations) #> [1] 689 18 xx <- filterMaxMarkers(xx, p = .25) @@ -180,15 +180,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/addLegend.html b/reference/addLegend.html index 9a6dbc41..166aaf27 100644 --- a/reference/addLegend.html +++ b/reference/addLegend.html @@ -1,9 +1,9 @@ -Adds a legend — addLegend • pRolocAdds a legend — addLegend • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -88,17 +88,19 @@

    Adds a legend

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is markers.

    -
    where
    +
    where

    One of "bottomleft" (default), "bottomright", "topleft", "topright" or "other" defining the location of the @@ -106,26 +108,22 @@

    Arguments

    other locations are passed to legend.

    -
    col
    +
    col

    A character defining point colours.

    -
    bty
    +
    bty

    Box type, as in legend. Default is set to "n".

    -
    ...
    +
    ...

    Additional parameters passed to legend.

    Value

    - - -

    Invisibly returns NULL

    - - +

    Invisibly returns NULL

    Details

    @@ -150,15 +148,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/addMarkers.html b/reference/addMarkers.html index 94399fd2..c1e05df9 100644 --- a/reference/addMarkers.html +++ b/reference/addMarkers.html @@ -1,5 +1,5 @@ -Adds markers to the data — addMarkers • pRolocAdds markers to the data — addMarkers • pRoloc - +
    @@ -33,7 +33,7 @@
    - +
    @@ -90,37 +90,37 @@

    Adds markers to the data

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    markers
    +
    markers

    A character with the name the markers' csv file or a named character of markers as provided by pRolocmarkers.

    -
    mcol
    +
    mcol

    A character of length 1 defining the feature variable label for the newly added markers. Default is "markers".

    -
    fcol
    +
    fcol

    An optional feature variable to be used to match against the markers. If missing, the feature names are used.

    -
    verbose
    +
    verbose

    A logical indicating if number of markers and marker table should be printed to the console.

    Value

    - - -

    A new instance of class MSnSet with an additional +

    A new instance of class MSnSet with an additional markers feature variable.

    @@ -193,15 +193,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/checkFeatureNamesOverlap.html b/reference/checkFeatureNamesOverlap.html index e3f54115..51a7f020 100644 --- a/reference/checkFeatureNamesOverlap.html +++ b/reference/checkFeatureNamesOverlap.html @@ -1,10 +1,10 @@ -Check feature names overlap — checkFeatureNamesOverlap • pRolocCheck feature names overlap — checkFeatureNamesOverlap • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,35 +82,35 @@

    Check feature names overlap

    Arguments

    -
    x
    + + +
    x

    An MSnSet instance.

    -
    y
    +
    y

    An MSnSet instance.

    -
    fcolx
    +
    fcolx

    The feature variable to separate unknown (fData(y)$coly == "unknown") from the marker features in the x object.

    -
    fcoly
    +
    fcoly

    As fcolx, for the y object. If missing, the value of fcolx is used.

    -
    verbose
    +
    verbose

    If TRUE (default), the overlap is printed out on the console.

    Value

    - - -

    Invisibly returns a named list of common markers, unique +

    Invisibly returns a named list of common markers, unique x markers, unique y markers in, common unknowns, unique x unknowns and unique y unknowns.

    @@ -157,15 +157,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/checkFvarOverlap.html b/reference/checkFvarOverlap.html index 6f9f89fe..6315f759 100644 --- a/reference/checkFvarOverlap.html +++ b/reference/checkFvarOverlap.html @@ -1,10 +1,10 @@ -Compare a feature variable overlap — checkFvarOverlap • pRolocCompare a feature variable overlap — checkFvarOverlap • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,35 +82,35 @@

    Compare a feature variable overlap

    Arguments

    -
    x
    + + +
    x

    An MSnSet instance.

    -
    y
    +
    y

    An MSnSet instance.

    -
    fcolx
    +
    fcolx

    The feature variable to separate unknown (fData(y)$coly == "unknown") from the marker features in the x object.

    -
    fcoly
    +
    fcoly

    As fcolx, for the y object. If missing, the value of fcolx is used.

    -
    verbose
    +
    verbose

    If TRUE (default), the contingency table of the the feature variables is printed out.

    Value

    - - -

    Invisibly returns a named list with the values of the +

    Invisibly returns a named list with the values of the diagonal, upper and lower triangles of the contingency table.

    @@ -155,15 +155,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/chi2-methods.html b/reference/chi2-methods.html index fc458598..6332e400 100644 --- a/reference/chi2-methods.html +++ b/reference/chi2-methods.html @@ -1,5 +1,5 @@ -The PCP 'chi square' method — chi2-methods • pRolocThe PCP 'chi square' method — chi2-methods • pRoloc - +
    @@ -39,7 +39,7 @@
    - +
    @@ -111,30 +111,30 @@

    Methods

    values; default, NULL, does not combine the \(Chi^2\) values.

    - +
    signature(x = "matrix", y = "numeric", method = "character", na.rm = "logical")

    Computes nrow(x) \(Chi^2\) values, for all the \((x_i, y)\) pairs. See above for the other arguments.

    - +
    signature(x = "numeric", y = "matrix", method = "character", na.rm = "logical")

    Computes nrow(y) \(Chi^2\) values, for all the \((x, y_i)\) pairs. See above for the other arguments.

    - +
    signature(x = "numeric", y = "numeric", method = "character", na.rm = "logical")

    Computes the \(Chi^2\) value for the \((x, y)\) pairs. See above for the other arguments.

    - +

    References

    -

    Andersen, J. S., Wilkinson, C. J., Mayor, T., Mortensen, P. et al., - Proteomic characterization of the human centrosome by protein +

    Andersen, J. S., Wilkinson, C. J., Mayor, T., Mortensen, P. et al., + Proteomic characterization of the human centrosome by protein correlation profiling. Nature 2003, 426, 570 - 574.

    Wiese, S., Gronemeyer, T., Ofman, R., Kunze, M. et al., Proteomics characterization of mouse kidney peroxisomes by tandem mass @@ -155,29 +155,29 @@

    Examples

    mrk <- rnorm(6)
     prot <- matrix(rnorm(60), ncol = 6)
     chi2(mrk, prot, method = "Andersen2003")
    -#>  [1] 1.0967902 1.1463540 1.4191266 0.2068751 1.0465213 0.4551556 0.5688080
    -#>  [8] 0.8372766 0.6537026 1.6546128
    +#>  [1] 0.6350206 1.2372019 0.6256963 1.2917212 1.6689025 0.4509966 2.9380140
    +#>  [8] 2.6580586 1.9303589 1.2332261
     chi2(mrk, prot, method = "Wiese2007")
    -#>  [1] -99.022514   1.259352   5.659525  -9.411881 -16.221333  -8.607184
    -#>  [7]  -8.761559 -12.617465 -16.665233 -61.464536
    +#>  [1]  8.303798 19.636347  7.704880  1.770659  7.995207  7.455503 10.919410
    +#>  [8] 22.276266 14.361729  2.902475
     
     pepmark <- matrix(rnorm(18), ncol = 6)
     pepprot <- matrix(rnorm(60), ncol = 6)
     chi2(pepmark, pepprot)
    -#>            [,1]      [,2]      [,3]
    -#>  [1,] 4.5988896 3.1499537 3.7396873
    -#>  [2,] 1.6701143 1.3216528 1.6257841
    -#>  [3,] 2.2106985 1.0001274 1.3839472
    -#>  [4,] 3.8488785 0.8428786 2.2532602
    -#>  [5,] 3.4259609 3.3506198 4.4293366
    -#>  [6,] 0.7326039 1.1720762 0.5971256
    -#>  [7,] 3.6745519 2.8268972 2.3971798
    -#>  [8,] 3.0170132 0.9467137 0.7544976
    -#>  [9,] 0.3171632 1.6979943 0.5115651
    -#> [10,] 2.4818172 1.3501409 1.4529782
    +#>           [,1]     [,2]     [,3]
    +#>  [1,] 2.359914 1.055094 1.454468
    +#>  [2,] 5.359839 3.192047 2.977774
    +#>  [3,] 2.225215 2.016703 3.105763
    +#>  [4,] 4.491698 3.618997 3.749134
    +#>  [5,] 2.902012 1.682787 1.601635
    +#>  [6,] 3.080319 1.533254 1.728296
    +#>  [7,] 1.423951 1.201455 2.078399
    +#>  [8,] 5.528471 3.571142 3.462414
    +#>  [9,] 3.511921 3.247617 3.412119
    +#> [10,] 4.036010 2.393212 2.255992
     chi2(pepmark, pepprot, fun = sum)
    -#>  [1] 11.488531  4.617551  4.594773  6.945017 11.205917  2.501806  8.898629
    -#>  [8]  4.718224  2.526723  5.284936
    +#>  [1]  4.869476 11.529660  7.347681 11.859828  6.186434  6.341870  4.703806
    +#>  [8] 12.562027 10.171657  8.685214
     
    @@ -192,15 +192,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/classWeights.html b/reference/classWeights.html index 7c4afa00..2758f736 100644 --- a/reference/classWeights.html +++ b/reference/classWeights.html @@ -1,5 +1,5 @@ -Calculate class weights — classWeights • pRolocCalculate class weights — classWeights • pRoloc - +
    @@ -33,7 +33,7 @@
    - +
    @@ -90,19 +90,19 @@

    Calculate class weights

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet

    -
    fcol
    +
    fcol

    The name of the features to be weighted

    Value

    - - -

    A table of class weights

    +

    A table of class weights

    Author

    @@ -150,15 +150,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    - +
    @@ -82,67 +82,67 @@

    Pairwise Distance Computation for Protein Information Sets

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    k
    -

    The number of clusters to try fitting to the protein set. +

    k
    +

    The number of clusters to try fitting to the protein set. Default is k = 1:5.

    -
    fcol
    +
    fcol

    The feature meta-data containing matrix of protein sets/ marker definitions. Default is GOAnnotations.

    -
    n
    +
    n

    The minimum number of proteins per set. If protein sets -contain less than n instances they will be ignored. +contain less than n instances they will be ignored. Defualt is 5.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    seed
    +
    seed

    An optional seed for the random number generator.

    Value

    - - -

    An instance of "ClustDistList" containing +

    An instance of "ClustDistList" containing a "ClustDist" instance for every protein set, which -summarises the algorithm information such as the number of k's tested -for the kmeans, and mean and normalised pairwise Euclidean distances +summarises the algorithm information such as the number of k's tested +for the kmeans, and mean and normalised pairwise Euclidean distances per numer of component clusters tested.

    Details

    -

    The input to the function is a MSnSet dataset -containing a matrix appended to the feature data slot -identifying the membership of protein instances to +

    The input to the function is a MSnSet dataset +containing a matrix appended to the feature data slot +identifying the membership of protein instances to a pre-defined set(s) e.g. a specific Gene Ontology term etc.

    -

    For each protein set, the clustDist function (i) -extracts all instances belonging to the set, (ii) using -the kmeans algorithm fits and tests k = c(1:5) -(default) cluster components to each set, (iii) calculates +

    For each protein set, the clustDist function (i) +extracts all instances belonging to the set, (ii) using +the kmeans algorithm fits and tests k = c(1:5) +(default) cluster components to each set, (iii) calculates the mean pairwise distance for each k tested.

    -

    Note: currently distances are calcualted in Euclidean space, +

    Note: currently distances are calcualted in Euclidean space, but other distance metrics will be supported in the future).

    -

    The output is a list of ClustDist objects, -one per information cluster. The ClustDist -class summarises the algorithm information such as the number of k's -tested for the kmeans, and mean and normalised pairwise Euclidean +

    The output is a list of ClustDist objects, +one per information cluster. The ClustDist +class summarises the algorithm information such as the number of k's +tested for the kmeans, and mean and normalised pairwise Euclidean distances per numer of component clusters tested. See ?ClustDist for more details.

    See also

    -

    For class definitions see "ClustDistList" +

    For class definitions see "ClustDistList" and "ClustDist".

    @@ -168,7 +168,7 @@

    Examples

    xx <- addGoAnnotations(dunkley2006, par) ## filter xx <- filterMinMarkers(xx, n = 50) -#> Retaining 18 out of 165 in GOAnnotations +#> Retaining 18 out of 166 in GOAnnotations xx <- filterMaxMarkers(xx, p = .25) #> Retaining 14 out of 18 in GOAnnotations ## get distances for protein sets @@ -205,15 +205,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/defunct.html b/reference/defunct.html index b0d892ef..9edf3efd 100644 --- a/reference/defunct.html +++ b/reference/defunct.html @@ -1,14 +1,14 @@ -pRoloc Deprecated and Defunct — Deprecated • pRolocpRoloc Deprecated and Defunct — Deprecated • pRoloc - +
    @@ -33,7 +33,7 @@
    - +

    The function, class, or data object you have asked for has been deprecated or made defunct.

    -

    Deprecated: minClassScore; use the replacement getPredictions

    +

    Deprecated: minClassScore; use the replacement getPredictions

    Defunct:

    Deprecated functions are provided for compatibility with older versions of the pRoloc package only, and will be defunct at the next release.

    @@ -98,15 +98,15 @@

    pRoloc Deprecated and Defunct

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/empPvalues.html b/reference/empPvalues.html index 14f1e3e9..21dbf098 100644 --- a/reference/empPvalues.html +++ b/reference/empPvalues.html @@ -1,12 +1,12 @@ -Estimate empirical p-values for \(Chi^2\) protein correlations. — empPvalues • pRolocEstimate empirical p-values for \(Chi^2\) protein correlations. — empPvalues • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    @@ -86,25 +86,24 @@

    Estimate empirical p-values for \(Chi^2\) protein correlations.

    Arguments

    -
    marker
    +

    +
    marker

    A numerics with markers relative intensities.

    -
    corMatrix
    +
    corMatrix

    A matrix of nrow(corMatrix) protein relative intensities to be compares against the marker.

    -
    n
    +
    n

    The number of iterations.

    -
    ...
    +
    ...

    Additional parameters to be passed to chi2.

    Value

    - - -

    A numeric of length nrow(corMatrix).

    +

    A numeric of length nrow(corMatrix).

    Author

    @@ -146,15 +145,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/fDataToUnknown.html b/reference/fDataToUnknown.html index 84c9f73d..e9b68b66 100644 --- a/reference/fDataToUnknown.html +++ b/reference/fDataToUnknown.html @@ -1,10 +1,10 @@ -Update a feature variable — fDataToUnknown • pRolocUpdate a feature variable — fDataToUnknown • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,17 +82,19 @@

    Update a feature variable

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    Feature variable to be modified. Default is "markers". If NULL, all feature variables will updated.

    -
    from
    +
    from

    A character defining the string or regular expression of the pattern to be replaced. Default is the empty string, i.e. the regular expression "^$". See @@ -100,20 +102,18 @@

    Arguments

    values are replaced by to.

    -
    to
    +
    to

    A replacement for matched pattern. Default is "unknown". See sub for details.

    -
    ...
    +
    ...

    Additional arguments passed to sub.

    Value

    - - -

    An updated MSnSet.

    +

    An updated MSnSet.

    Author

    @@ -152,15 +152,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/filterBinMSnSet.html b/reference/filterBinMSnSet.html index 86e08ec0..ee545678 100644 --- a/reference/filterBinMSnSet.html +++ b/reference/filterBinMSnSet.html @@ -1,10 +1,10 @@ -Filter a binary MSnSet — filterBinMSnSet • pRolocFilter a binary MSnSet — filterBinMSnSet • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,35 +82,35 @@

    Filter a binary MSnSet

    Arguments

    -
    object
    + + +
    object

    An MSnSet

    -
    MARGIN
    +
    MARGIN

    1 or 2. Default is 2.

    -
    t
    +
    t

    Rows/columns that have t or less 1s, it will be filtered out. When t and q are missing, default is to use t = 1.

    -
    q
    +
    q

    If a row has a higher quantile than defined by q, it will be filtered out.

    -
    verbose
    +
    verbose

    A logical defining of a message is to be printed. Default is TRUE.

    Value

    - - -

    A filtered MSnSet.

    +

    A filtered MSnSet.

    See also

    @@ -197,15 +197,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/filterMaxMarkers.html b/reference/filterMaxMarkers.html index 4c93f320..97ec7bc7 100644 --- a/reference/filterMaxMarkers.html +++ b/reference/filterMaxMarkers.html @@ -1,10 +1,10 @@ -Removes class/annotation information from a matrix of candidate markers that appear in the fData. — filterMaxMarkers • pRolocRemoves class/annotation information from a matrix of candidate markers that appear in the fData. — filterMaxMarkers • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,39 +82,37 @@

    Removes class/annotation information from a matrix of candidate markers that

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    n
    +
    n

    Maximum number of proteins allowed per class/information term.

    -
    p
    -

    Maximum percentage of proteins per column. Default is 0.2 +

    p
    +

    Maximum percentage of proteins per column. Default is 0.2 i.e. remove columns that have information for greater than 20 of the total number of proteins in the dataset (note: this is useful for example, if information is GO terms, for removing very general and uninformative terms).

    -
    fcol
    +
    fcol

    The name of the matrix of marker information. Default is GOAnnotations.

    -
    verbose
    +
    verbose

    Number of marker candidates retained after filtering.

    Value

    - - -

    An updated MSnSet

    - - +

    An updated MSnSet

    See also

    @@ -133,15 +131,15 @@

    See also

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/filterMinMarkers.html b/reference/filterMinMarkers.html index 7fd3737d..24d7bab4 100644 --- a/reference/filterMinMarkers.html +++ b/reference/filterMinMarkers.html @@ -1,10 +1,10 @@ -Removes class/annotation information from a matrix of candidate markers that appear in the fData. — filterMinMarkers • pRolocRemoves class/annotation information from a matrix of candidate markers that appear in the fData. — filterMinMarkers • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,32 +82,32 @@

    Removes class/annotation information from a matrix of candidate markers that

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    n
    +
    n

    Minimum number of proteins allowed per column. Default is 10.

    -
    p
    +
    p

    Minimum percentage of proteins per column.

    -
    fcol
    +
    fcol

    The name of the matrix of marker information. Default is GOAnnotations.

    -
    verbose
    +
    verbose

    Number of marker candidates retained after filtering.

    Value

    - - -

    An updated MSnSet.

    +

    An updated MSnSet.

    See also

    @@ -130,15 +130,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/filterZeroCols.html b/reference/filterZeroCols.html index ff37f25e..e2100656 100644 --- a/reference/filterZeroCols.html +++ b/reference/filterZeroCols.html @@ -1,10 +1,10 @@ -Remove 0 columns/rows — filterZeroCols • pRolocRemove 0 columns/rows — filterZeroCols • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -84,20 +84,20 @@

    Remove 0 columns/rows

    Arguments

    -
    object
    + + +
    object

    A MSnSet object.

    -
    verbose
    +
    verbose

    Print a message with the number of filtered out columns/row (if any).

    Value

    - - -

    An MSnSet.

    +

    An MSnSet.

    Author

    @@ -130,15 +130,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/getGOFromFeatures.html b/reference/getGOFromFeatures.html index 1a5867a0..331847d4 100644 --- a/reference/getGOFromFeatures.html +++ b/reference/getGOFromFeatures.html @@ -1,10 +1,10 @@ -Retrieve GO terms for feature names — getGOFromFeatures • pRolocRetrieve GO terms for feature names — getGOFromFeatures • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -89,36 +89,38 @@

    Retrieve GO terms for feature names

    Arguments

    -
    id
    + + +
    id

    An character with feature names to be pulled from biomart. If and MSnSet is provided, then featureNames(id) is used.

    -
    namespace
    +
    namespace

    The GO namespace. One of biological_process, cellular_component (default) or molecular_function.

    -
    evidence
    +
    evidence

    The GO evidence code. See showGOEvidenceCodes for details. If NULL (default), no filtering based on the evidence code is performed.

    -
    params
    +
    params

    An instance of class "AnnotationParams".

    -
    verbose
    +
    verbose

    A logical defining verbosity of the function. Default is FALSE.

    -
    nmax
    +
    nmax

    As described in https://support.bioconductor.org/p/86358/, the Biomart result can be unreliable for large queries. This argument @@ -128,9 +130,7 @@

    Arguments

    Value

    - - -

    A data.frame with relevant GO terms.

    +

    A data.frame with relevant GO terms.

    Author

    @@ -172,8 +172,8 @@

    Examples

    #> 37 AT1G51760 GO:0005634 cellular_component #> 38 AT1G51760 GO:0005788 cellular_component #> 40 AT1G51760 GO:0005783 cellular_component -#> 44 AT1G56340 GO:0005783 cellular_component -#> 48 AT1G56340 GO:0005788 cellular_component +#> 45 AT1G56340 GO:0005783 cellular_component +#> 49 AT1G56340 GO:0005788 cellular_component #> 51 AT1G56340 GO:0005634 cellular_component #> 52 AT1G56340 GO:0009506 cellular_component #> 53 AT1G56340 GO:0005783 cellular_component @@ -207,8 +207,8 @@

    Examples

    #> 37 nucleus HDA #> 38 endoplasmic reticulum lumen IEA #> 40 endoplasmic reticulum HDA -#> 44 endoplasmic reticulum IEA -#> 48 endoplasmic reticulum lumen IEA +#> 45 endoplasmic reticulum IEA +#> 49 endoplasmic reticulum lumen IEA #> 51 nucleus HDA #> 52 plasmodesma HDA #> 53 endoplasmic reticulum HDA @@ -234,15 +234,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/getMarkerClasses.html b/reference/getMarkerClasses.html index d256a998..c71a2c3e 100644 --- a/reference/getMarkerClasses.html +++ b/reference/getMarkerClasses.html @@ -1,11 +1,11 @@ -Returns the organelle classes in an 'MSnSet' — getMarkerClasses • pRolocReturns the organelle classes in an 'MSnSet' — getMarkerClasses • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -84,24 +84,24 @@

    Returns the organelle classes in an 'MSnSet'

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The name of the markers column in the featureData slot. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to sort from the base package.

    Value

    - - -

    A character vector of the organelle classes in the data.

    +

    A character vector of the organelle classes in the data.

    See also

    @@ -137,15 +137,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/getMarkers.html b/reference/getMarkers.html index 45afa55c..0fd8054f 100644 --- a/reference/getMarkers.html +++ b/reference/getMarkers.html @@ -1,11 +1,11 @@ -Get the organelle markers in an MSnSet — getMarkers • pRolocGet the organelle markers in an MSnSet — getMarkers • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -84,30 +84,30 @@

    Get the organelle markers in an MSnSet

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The name of the markers column in the featureData slot. Default is "markers".

    -
    names
    +
    names

    A logical indicating if the markers vector should be named. Ignored if markers are encoded as a matrix.

    -
    verbose
    +
    verbose

    If TRUE, a marker table is printed and the markers are returned invisibly. If FALSE, the markers are returned.

    Value

    - - -

    A character (matrix) of length (ncol) +

    A character (matrix) of length (ncol) ncol(object), depending on the vector or matrix encoding of the markers.

    @@ -178,15 +178,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/getNormDist.html b/reference/getNormDist.html index ad4e0c65..178692d1 100644 --- a/reference/getNormDist.html +++ b/reference/getNormDist.html @@ -1,10 +1,10 @@ -Extract Distances from a "ClustDistList" object — getNormDist • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,19 +82,19 @@

    Extract Distances from a "ClustDistList" object

    Arguments

    -
    object
    + + +
    object

    An instance of class "ClustDistList".

    -
    p
    +
    p

    The normalisation factor. Default is 1/3.

    Value

    - - -

    An numeric of normalised distances, one per protein set in the +

    An numeric of normalised distances, one per protein set in the ClustDistList.

    @@ -119,15 +119,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/getPredictions.html b/reference/getPredictions.html index a90656a2..e0e12d98 100644 --- a/reference/getPredictions.html +++ b/reference/getPredictions.html @@ -1,11 +1,11 @@ -Returns the predictions in an 'MSnSet' — getPredictions • pRolocReturns the predictions in an 'MSnSet' — getPredictions • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -84,27 +84,29 @@

    Returns the predictions in an 'MSnSet'

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The name of the prediction column in the featureData slot.

    -
    scol
    +
    scol

    The name of the prediction score column in the featureData slot. If missing, created by pasting '.scores' after fcol.

    -
    mcol
    -

    The feature meta data column containing the labelled training +

    mcol
    +

    The feature meta data column containing the labelled training data.

    -
    t
    +
    t

    The score threshold. Predictions with score < t are set to 'unknown'. Default is 0. It is also possible to define thresholds for each prediction class, in which case, t is a @@ -112,7 +114,7 @@

    Arguments

    class names.

    -
    verbose
    +
    verbose

    If TRUE, a prediction table is printed and the predictions are returned invisibly. If FALSE, the predictions are returned.

    @@ -120,9 +122,7 @@

    Arguments

    Value

    - - -

    An instance of class "MSnSet" with fcol.pred feature +

    An instance of class "MSnSet" with fcol.pred feature variable storing the prediction results according to the chosen threshold.

    @@ -174,8 +174,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed svm prediction (cost=0.5 sigma=0.1) Tue Mar 12 07:13:29 2024 -#> Added svm predictions according to global threshold = 0 Tue Mar 12 07:13:29 2024 +#> Performed svm prediction (cost=0.5 sigma=0.1) Sun Jun 16 09:17:48 2024 +#> Added svm predictions according to global threshold = 0 Sun Jun 16 09:17:48 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "svm", t = .9) ## single threshold #> ans @@ -202,8 +202,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed svm prediction (cost=0.5 sigma=0.1) Tue Mar 12 07:13:29 2024 -#> Added svm predictions according to global threshold = 0.9 Tue Mar 12 07:13:29 2024 +#> Performed svm prediction (cost=0.5 sigma=0.1) Sun Jun 16 09:17:48 2024 +#> Added svm predictions according to global threshold = 0.9 Sun Jun 16 09:17:48 2024 #> MSnbase version: 1.17.12 ## 50% top predictions per class ts <- orgQuants(res, fcol = "svm", t = .5) @@ -236,8 +236,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed svm prediction (cost=0.5 sigma=0.1) Tue Mar 12 07:13:29 2024 -#> Added svm predictions according to thresholds: ER lumen = 0.30, ER membrane = 0.84, Golgi = 0.78, Mitochondrion = 0.75, PM = 0.73, Plastid = 0.77, Ribosome = 0.54, TGN = 0.53, vacuole = 0.57 Tue Mar 12 07:13:29 2024 +#> Performed svm prediction (cost=0.5 sigma=0.1) Sun Jun 16 09:17:48 2024 +#> Added svm predictions according to thresholds: ER lumen = 0.30, ER membrane = 0.84, Golgi = 0.78, Mitochondrion = 0.75, PM = 0.73, Plastid = 0.77, Ribosome = 0.54, TGN = 0.53, vacuole = 0.57 Sun Jun 16 09:17:48 2024 #> MSnbase version: 1.17.12
    @@ -253,15 +253,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/getStockcol.html b/reference/getStockcol.html index 421fc646..3f5319ff 100644 --- a/reference/getStockcol.html +++ b/reference/getStockcol.html @@ -1,5 +1,5 @@ -Manage default colours and point characters — setLisacol • pRolocManage default colours and point characters — setLisacol • pRoloc - +
    @@ -33,7 +33,7 @@
    - +
    @@ -112,36 +112,34 @@

    Manage default colours and point characters

    Arguments

    -
    cols
    + + +
    cols

    A vector of colour characters or NULL, which sets the colours to the default values.

    -
    pchs
    +
    pchs

    A vector of numeric or NULL, which sets the point characters to the default values.

    -
    col
    +
    col

    A colour character or NULL, which sets the colour to #E7E7E7 (grey91), the default colour for unknown features.

    -
    pch
    +
    pch

    A numeric vector of length 1 or NULL, which sets the point character to 21, the default.

    Value

    - - -

    The set functions set (and invisibly returns) -colours. The get functions returns a character

    - - -

    vector of colours. For the pch functions, numerics +

    The set functions set (and invisibly returns) +colours. The get functions returns a character +vector of colours. For the pch functions, numerics rather than characters.

    @@ -201,15 +199,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/goIdToTerm.html b/reference/goIdToTerm.html index 2321991d..e3056e3a 100644 --- a/reference/goIdToTerm.html +++ b/reference/goIdToTerm.html @@ -1,10 +1,10 @@ -Convert GO ids to/from terms — goIdToTerm • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -88,26 +88,26 @@

    Convert GO ids to/from terms

    Arguments

    -
    x
    + + +
    x

    A character of GO ids or terms.

    -
    names
    +
    names

    Should a named character be returned? Default is TRUE.

    -
    keepNA
    +
    keepNA

    Should any GO term/id names that are missing or obsolete -be replaced with a NA? Default is TRUE. If +be replaced with a NA? Default is TRUE. If FALSE then the GO term/id names is kept.

    Value

    - - -

    A character of GO terms (ids) if x were ids +

    A character of GO terms (ids) if x were ids (terms).

    @@ -164,15 +164,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/highlightOnPlot.html b/reference/highlightOnPlot.html index c99daca2..35d67ee2 100644 --- a/reference/highlightOnPlot.html +++ b/reference/highlightOnPlot.html @@ -1,5 +1,5 @@ -Highlight features of interest on a spatial proteomics plot — highlightOnPlot • pRolocHighlight features of interest on a spatial proteomics plot — highlightOnPlot • pRoloc - +
    @@ -32,7 +32,7 @@
    - +
    @@ -90,18 +90,20 @@

    Highlight features of interest on a spatial proteomics plot

    Arguments

    -
    object
    + + +
    object

    The main dataset described as an MSnSet or a matrix with the coordinates of the features on the PCA plot produced (and invisibly returned) by plot2D.

    -
    foi
    +
    foi

    An instance of FeaturesOfInterest, or, alternatively, a character of feautre names.

    -
    labels
    +
    labels

    A character of length 1 with a feature variable name to be used to label the features of interest. This is only valid if object is an @@ -111,21 +113,21 @@

    Arguments

    missing, which does not add any label.s

    -
    args
    +
    args

    A named list of arguments to be passed to plot2D if the PCA coordinates are to be calculated. Ignored if the PCA coordinates are passed directly, i.e. object is a matrix.

    -
    ...
    +
    ...

    Additional parameters passed to points or text (when labels is TRUE) when adding to plot2D, or spheres3d or text3d when adding the plot3D

    -
    radius
    +
    radius

    Radius of the spheres to be added to the visualisation produced by plot3D. Default is 0.3 (i.e plot3D's radius1 * 3), to emphasise the features @@ -135,9 +137,7 @@

    Arguments

    Value

    - - -

    NULL; used for its side effects.

    +

    NULL; used for its side effects.

    Author

    @@ -211,15 +211,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/index.html b/reference/index.html index 3ac7eb17..2de29c93 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,9 +1,9 @@ -Function reference • pRolocPackage index • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -405,15 +405,15 @@

    All functions
    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    @@ -28,7 +28,7 @@
    - +
    @@ -87,44 +87,43 @@

    knn classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by knnOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    k
    +
    k

    If assessRes is missing, a k must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to knn from package class.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    knn and knn.scores feature variables storing the +

    An instance of class "MSnSet" with + knn and knn.scores feature variables storing the classification results and scores respectively.

    @@ -190,8 +189,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed knn prediction (k=3) Tue Mar 12 07:13:32 2024 -#> Added knn predictions according to global threshold = 0 Tue Mar 12 07:13:32 2024 +#> Performed knn prediction (k=3) Sun Jun 16 09:17:52 2024 +#> Added knn predictions according to global threshold = 0 Sun Jun 16 09:17:52 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "knn", t = 0.75) #> ans @@ -218,8 +217,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed knn prediction (k=3) Tue Mar 12 07:13:32 2024 -#> Added knn predictions according to global threshold = 0.75 Tue Mar 12 07:13:32 2024 +#> Performed knn prediction (k=3) Sun Jun 16 09:17:52 2024 +#> Added knn predictions according to global threshold = 0.75 Sun Jun 16 09:17:52 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "knn") @@ -237,15 +236,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/knnOptimisation.html b/reference/knnOptimisation.html index ed3f7f02..ac4ea923 100644 --- a/reference/knnOptimisation.html +++ b/reference/knnOptimisation.html @@ -1,10 +1,10 @@ -knn parameter optimisation — knnOptimisation • pRolocknn parameter optimisation — knnOptimisation • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -93,53 +93,53 @@

    knn parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    k
    +
    k

    The hyper-parameter. Default values are seq(3, 15, 2).

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to knn from package class.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -172,15 +172,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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b/reference/knntlClassification.html @@ -1,10 +1,10 @@ -knn transfer learning classification — knntlClassification • pRolocknn transfer learning classification — knntlClassification • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -90,47 +90,47 @@

    knn transfer learning classification

    Arguments

    -
    primary
    + + +
    primary

    An instance of class "MSnSet".

    -
    auxiliary
    +
    auxiliary

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    bestTheta
    +
    bestTheta

    Best theta vector as output from knntlOptimisation, see knntlOptimisation for details

    -
    k
    +
    k

    Numeric vector of length 2, containing the best k parameters to use for the primary and auxiliary datasets. If k k is not specified it will be calculated internally.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    seed
    +
    seed

    The optional random number generator seed.

    Value

    - - -

    A character vector of the classifications for the unknowns

    +

    A character vector of the classifications for the unknowns

    See also

    @@ -179,11 +179,11 @@

    Examples

    #> Results #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> 0.8789 0.8805 0.8821 0.8821 0.8838 0.8854 +#> 0.8638 0.8692 0.8746 0.8746 0.8800 0.8854 #> best theta: #> ER Golgi Mitochondrion PM -#> weight:0 1 2 0 2 -#> weight:1 1 0 2 0 +#> weight:0 2 2 0 2 +#> weight:1 0 0 2 0 th <- getParams(opt) plot(opt) @@ -224,15 +224,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/knntlOptimisation.html b/reference/knntlOptimisation.html index 079a5927..45975d28 100644 --- a/reference/knntlOptimisation.html +++ b/reference/knntlOptimisation.html @@ -1,10 +1,10 @@ -theta parameter optimisation — knntlOptimisation • pRoloctheta parameter optimisation — knntlOptimisation • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -98,53 +98,55 @@

    theta parameter optimisation

    Arguments

    -
    primary
    + + +
    primary

    An instance of class "MSnSet".

    -
    auxiliary
    +
    auxiliary

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    k
    +
    k

    Numeric vector of length 2, containing the best k parameters to use for the primary (k[1]) and auxiliary (k[2]) datasets. See knnOptimisation for generating best k.

    -
    times
    +
    times

    The number of times cross-validation is performed. Default is 50.

    -
    test.size
    +
    test.size

    The size of test (validation) data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The number of rounds of cross-validation to perform.

    -
    by
    +
    by

    The increment for theta, must be one of c(1, 0.5, 0.25, 0.2, 0.15, 0.1, 0.05)

    -
    length.out
    +
    length.out

    Alternative to using by parameter. Specifies the desired length of the sequence of theta to test.

    -
    th
    +
    th

    A matrix of theta values to test for each class as generated from the function thetas, the number of columns should be equal to the number of classes contained @@ -153,40 +155,38 @@

    Arguments

    valid if the default method 'Breckels' is used.

    -
    xfolds
    +
    xfolds

    Option to pass specific folds for the cross validation.

    -
    BPPARAM
    +
    BPPARAM

    Required for parallelisation. If not specified selects a default BiocParallelParam, from global options or, if that fails, the most recently registered() back-end.

    -
    method
    +
    method

    The k-NN transfer learning method to use. The default is 'Breckels' as described in the Breckels et al (2016). If 'Wu' is specificed then the original method implemented Wu and Dietterich (2004) is implemented.

    -
    log
    +
    log

    A logical defining whether logging should be enabled. Default is FALSE. Note that logging produes considerably bigger objects.

    -
    seed
    +
    seed

    The optional random number generator seed.

    Value

    - - -

    A list of containing the theta combinations tested, +

    A list of containing the theta combinations tested, associated macro F1 score and accuracy for each combination over each round (specified by times).

    @@ -231,15 +231,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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a/reference/ksvmClassification.html +++ b/reference/ksvmClassification.html @@ -1,10 +1,10 @@ -ksvm classification — ksvmClassification • pRolocksvm classification — ksvmClassification • pRoloc - +

    - +
    @@ -89,44 +89,43 @@

    ksvm classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by ksvmOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    cost
    +
    cost

    If assessRes is missing, a cost must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to ksvm from package kernlab.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    ksvm and ksvm.scores feature variables storing +

    An instance of class "MSnSet" with + ksvm and ksvm.scores feature variables storing the classification results and scores respectively.

    @@ -152,27 +151,27 @@

    Examples

    #> Results #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> 0.9529 0.9656 0.9783 0.9770 0.9891 1.0000 -#> best cost: 0.5 16 +#> 0.8804 0.9123 0.9443 0.9255 0.9480 0.9518 +#> best cost: 0.5 plot(params) f1Count(params) #> -#> 16 -#> 1 +#> 0.5 +#> 1 levelPlot(params) getParams(params) #> cost -#> 16 +#> 0.5 res <- ksvmClassification(dunkley2006, params) #> [1] "markers" getPredictions(res, fcol = "ksvm") #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 23 193 113 150 47 +#> 19 187 132 130 58 #> Plastid Ribosome TGN vacuole -#> 54 19 69 21 +#> 55 19 68 21 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -192,15 +191,15 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed ksvm prediction (cost=16) Tue Mar 12 07:13:47 2024 -#> Added ksvm predictions according to global threshold = 0 Tue Mar 12 07:13:47 2024 +#> Performed ksvm prediction (cost=0.5) Sun Jun 16 09:18:06 2024 +#> Added ksvm predictions according to global threshold = 0 Sun Jun 16 09:18:06 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "ksvm", t = 0.75) #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 14 158 73 55 46 +#> 14 157 64 55 46 #> Plastid Ribosome TGN unknown vacuole -#> 20 19 13 270 21 +#> 20 19 13 280 21 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -220,8 +219,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed ksvm prediction (cost=16) Tue Mar 12 07:13:47 2024 -#> Added ksvm predictions according to global threshold = 0.75 Tue Mar 12 07:13:47 2024 +#> Performed ksvm prediction (cost=0.5) Sun Jun 16 09:18:06 2024 +#> Added ksvm predictions according to global threshold = 0.75 Sun Jun 16 09:18:06 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "ksvm") @@ -239,15 +238,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/ksvmOptimisation.html b/reference/ksvmOptimisation.html index a2cf2064..c18c81a5 100644 --- a/reference/ksvmOptimisation.html +++ b/reference/ksvmOptimisation.html @@ -1,10 +1,10 @@ -ksvm parameter optimisation — ksvmOptimisation • pRolocksvm parameter optimisation — ksvmOptimisation • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -93,53 +93,53 @@

    ksvm parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    cost
    +
    cost

    The hyper-parameter. Default values are 2^-4:4.

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to ksvm from package kernlab.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -172,15 +172,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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z@~CS?zOj5UAFJ!9sj*q-`V4{a@z1E@Z#v0-_YXeuZ@=^Z!w(#*Zr)Zg=;f}8Q&Yt4 PGrD8-#{`a=_w)Y&{b#aM diff --git a/reference/makeGoSet.html b/reference/makeGoSet.html index 5d2dc2b3..72fb9d1d 100644 --- a/reference/makeGoSet.html +++ b/reference/makeGoSet.html @@ -1,10 +1,10 @@ -Creates a GO feature MSnSet — makeGoSet • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -82,31 +82,31 @@

    Creates a GO feature MSnSet

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet" or a character of feature names.

    -
    params
    +
    params

    An instance of class "AnnotationParams", compatible with featureNames(object)'s format.

    -
    namespace
    +
    namespace

    The ontology name space. One or several of "biological_process", "cellular_component" or "molecular_function".

    -
    evidence
    +
    evidence

    GO evidence filtering.

    Value

    - - -

    A new "MSnSet" with the GO terms +

    A new "MSnSet" with the GO terms for the respective features in the original object.

    @@ -123,7 +123,7 @@

    Examples

    dunkley2006params) goset #> MSnSet (storageMode: lockedEnvironment) -#> assayData: 10 features, 21 samples +#> assayData: 10 features, 20 samples #> element names: exprs #> protocolData: none #> phenoData: none @@ -134,8 +134,8 @@

    Examples

    #> experimentData: use 'experimentData(object)' #> Annotation: #> - - - Processing information - - - -#> Constructed GO set using cellular_component namespace [Tue Mar 12 07:13:48 2024] -#> MSnbase version: 2.29.3 +#> Constructed GO set using cellular_component namespace [Sun Jun 16 09:18:07 2024] +#> MSnbase version: 2.31.1 exprs(goset)[1:10, 1:5] #> GO:0005783 GO:0005788 GO:0005576 GO:0005739 GO:0099503 #> AT1G09210 1 1 1 1 1 @@ -164,15 +164,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/markerMSnSet.html b/reference/markerMSnSet.html index 061c8b39..04785b9d 100644 --- a/reference/markerMSnSet.html +++ b/reference/markerMSnSet.html @@ -1,10 +1,10 @@ -Extract marker/unknown subsets — markerMSnSet • pRolocExtract marker/unknown subsets — markerMSnSet • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -84,11 +84,13 @@

    Extract marker/unknown subsets

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet

    -
    fcol
    +
    fcol

    The name of the feature data column, that will be used to separate the markers from the proteins of unknown localisation. When the markers are encoded as vectors, features of @@ -100,9 +102,7 @@

    Arguments

    Value

    - - -

    An new MSnSet with marker/unknown proteins only.

    +

    An new MSnSet with marker/unknown proteins only.

    See also

    @@ -166,15 +166,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/markers.html b/reference/markers.html index d425f2a6..2150f07f 100644 --- a/reference/markers.html +++ b/reference/markers.html @@ -1,10 +1,10 @@ -Create a marker vector or matrix. — mrkVecToMat • pRolocCreate a marker vector or matrix. — mrkVecToMat • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -94,29 +94,29 @@

    Create a marker vector or matrix.

    Arguments

    -
    object
    + + +
    object

    An MSnSet object

    -
    vfcol
    +
    vfcol

    The name of the vector marker feature variable. Default is "markers".

    -
    mfcol
    +
    mfcol

    The name of the matrix marker feature variable. Default is "Markers".

    -
    fcol
    +
    fcol

    A marker feature variable name.

    Value

    - - -

    An updated MSnSet with a new vector (matrix) marker +

    An updated MSnSet with a new vector (matrix) marker set.

    @@ -205,15 +205,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/mcmc-helpers.html b/reference/mcmc-helpers.html index 0d3cf046..9097eccf 100644 --- a/reference/mcmc-helpers.html +++ b/reference/mcmc-helpers.html @@ -1,5 +1,5 @@ -Number of outlier at each iteration of MCMC — mcmc_get_outliers • pRolocNumber of outlier at each iteration of MCMC — mcmc_get_outliers • pRoloc - +
    @@ -40,7 +40,7 @@
    - +
    @@ -113,69 +113,55 @@

    Number of outlier at each iteration of MCMC

    mcmc_thin_chains(x, freq = 5) -# S4 method for MCMCParams,character +# S4 method for class 'MCMCParams,character' plot(x, y, ...)

    Arguments

    -
    x
    + + +
    x

    Object of class MCMCParams

    -
    k
    +
    k

    A list of coda::mcmc objects, as returned by mcmc_get_outliers, mcmc_get_meanComponent and mcmc_get_meanoutliersProb.

    -
    param
    +
    param

    An object of class MCMCParams.

    -
    n
    +
    n

    integer(1) defining number of iterations to burn. The default is 50

    -
    freq
    +
    freq

    Thinning frequency. The function retains every `freq`th iteration and is an `integer(1)`. The default thinning frequency is `5`.

    -
    y
    +
    y

    A `character(1)` with a protein name.

    -
    ...
    +
    ...

    Currently ignored.

    Value

    - - -

    A list of length length(x).

    - - +

    A list of length length(x).

    A list of length length(x).

    - -

    A list of length length(x).

    - -

    A matrix with the test z- and p-values for each chain.

    - -

    A pooled MCMCParams object.

    - -

    An updated MCMCParams object.

    - -

    A thinned `MCMCParams` object.

    - -

    A ggplot2 object.

    @@ -195,15 +181,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/minMarkers.html b/reference/minMarkers.html index f5def6da..429da4b5 100644 --- a/reference/minMarkers.html +++ b/reference/minMarkers.html @@ -1,11 +1,11 @@ -Creates a reduced marker variable — minMarkers • pRolocCreates a reduced marker variable — minMarkers • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -84,24 +84,24 @@

    Creates a reduced marker variable

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    n
    +
    n

    Minumum of marker instances per class.

    -
    fcol
    +
    fcol

    The name of the markers column in the featureData slot. Default is markers.

    Value

    - - -

    An instance of class "MSnSet" with a new +

    An instance of class "MSnSet" with a new feature variables, named after the original fcol variable and the n value.

    @@ -146,15 +146,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/mixing_posterior_check.html b/reference/mixing_posterior_check.html index 0954f1c7..2c325f41 100644 --- a/reference/mixing_posterior_check.html +++ b/reference/mixing_posterior_check.html @@ -1,9 +1,9 @@ -Model calibration plots — mixing_posterior_check • pRolocModel calibration plots — mixing_posterior_check • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -80,28 +80,28 @@

    Model calibration plots

    Arguments

    -
    object
    + + +
    object

    A valid object of class MSnset

    -
    params
    +
    params

    A valid object of class MCMCParams that has been processed and checked for convergence

    -
    priors
    +
    priors

    The prior that were used in the model

    -
    fcol
    +
    fcol

    The columns of the feature data which contain the marker data.

    Value

    - - -

    Used for side effect of producing plot. Invisibily returns an ggplot object +

    Used for side effect of producing plot. Invisibily returns an ggplot object that can be further manipulated

    @@ -111,7 +111,7 @@

    Author

    Examples

    -
    if (FALSE) {
    +    
    if (FALSE) { # \dontrun{
     library("pRoloc")
     data("tan2009r1")
     
    @@ -122,7 +122,7 @@ 

    Examples

    myparams2 <- chains(mcmc_pool_chains(tanres))[[1]] priors <- tanres@priors pRoloc:::mixing_posterior_check(object = tan2009r1, params = myparams2, priors = priors) -} +} # }
    @@ -137,15 +137,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/move2Ds.html b/reference/move2Ds.html index b7098694..ae6c2de3 100644 --- a/reference/move2Ds.html +++ b/reference/move2Ds.html @@ -1,11 +1,11 @@ -Displays a spatial proteomics animation — move2Ds • pRolocDisplays a spatial proteomics animation — move2Ds • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -84,14 +84,16 @@

    Displays a spatial proteomics animation

    Arguments

    -
    object
    + + +
    object

    An linkS4class{MSnSet} or a MSnSetList. In the latter case, only the two first elements of the list will be used for plotting and the others will be silently ignored.

    -
    pcol
    +
    pcol

    If object is an MSnSet, a factor or the name of a phenotype variable (phenoData slot) defining how to split the single MSnSet into two or more @@ -99,17 +101,17 @@

    Arguments

    MSnSetList.

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is markers. Use NULL to suppress any colouring.

    -
    n
    +
    n

    Number of frames, Default is 25.

    -
    hl
    +
    hl

    An optional instance of class linkS4class{FeaturesOfInterest} to track features of interest.

    @@ -117,9 +119,7 @@

    Arguments

    Value

    - - -

    Used for its side effect of producing a short animation.

    +

    Used for its side effect of producing a short animation.

    See also

    @@ -231,15 +231,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/mrkConsProfiles.html b/reference/mrkConsProfiles.html index 5e4e897c..aacc1ecf 100644 --- a/reference/mrkConsProfiles.html +++ b/reference/mrkConsProfiles.html @@ -1,9 +1,9 @@ -Marker consensus profiles — mrkConsProfiles • pRolocMarker consensus profiles — mrkConsProfiles • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -80,29 +80,27 @@

    Marker consensus profiles

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is markers.

    -
    method
    +
    method

    A function to average marker profiles. Default is mean.

    Value

    - - -

    A matrix of dimensions number of clusters

    - - -

    (exluding unknowns) by number of fractions.

    +

    A matrix of dimensions number of clusters + (exluding unknowns) by number of fractions.

    See also

    @@ -206,15 +204,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/mrkHClust.html b/reference/mrkHClust.html index e9c8b528..f10bcf10 100644 --- a/reference/mrkHClust.html +++ b/reference/mrkHClust.html @@ -1,5 +1,5 @@ -Draw a dendrogram of subcellular clusters — mrkHClust • pRolocDraw a dendrogram of subcellular clusters — mrkHClust • pRoloc - +
    @@ -34,7 +34,7 @@
    - +
    @@ -100,46 +100,46 @@

    Draw a dendrogram of subcellular clusters

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is markers.

    -
    distargs
    +
    distargs

    A list of arguments to be passed to the dist function.

    -
    hclustargs
    +
    hclustargs

    A list of arguments to be passed to the hclust function.

    -
    method
    +
    method

    A function to average marker profiles. Default is mean.

    -
    plot
    +
    plot

    A logical defining whether the dendrogram should be plotted. Default is TRUE.

    -
    ...
    +
    ...

    Additional parameters passed when plotting the dendrogram.

    Value

    - - -

    Invisibly returns a dendrogram object, containing the +

    Invisibly returns a dendrogram object, containing the hierarchical cluster as computed by hclust.

    @@ -167,15 +167,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    @@ -28,7 +28,7 @@
    - +
    @@ -87,44 +87,43 @@

    nb classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by nbOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    laplace
    +
    laplace

    If assessRes is missing, a laplace must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to naiveBayes from package e1071.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    nb and nb.scores feature variables storing the +

    An instance of class "MSnSet" with + nb and nb.scores feature variables storing the classification results and scores respectively.

    @@ -150,8 +149,8 @@

    Examples

    #> Results #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> 0.9377 0.9516 0.9654 0.9677 0.9827 1.0000 -#> best laplace: 0 5 +#> 0.9654 0.9686 0.9718 0.9791 0.9859 1.0000 +#> best laplace: 5 0 plot(params) f1Count(params) @@ -190,8 +189,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed naiveBayes prediction (laplace=5) Tue Mar 12 07:14:00 2024 -#> Added naiveBayes predictions according to global threshold = 0 Tue Mar 12 07:14:00 2024 +#> Performed naiveBayes prediction (laplace=5) Sun Jun 16 09:18:19 2024 +#> Added naiveBayes predictions according to global threshold = 0 Sun Jun 16 09:18:19 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "naiveBayes", t = 1) #> ans @@ -218,8 +217,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed naiveBayes prediction (laplace=5) Tue Mar 12 07:14:00 2024 -#> Added naiveBayes predictions according to global threshold = 1 Tue Mar 12 07:14:00 2024 +#> Performed naiveBayes prediction (laplace=5) Sun Jun 16 09:18:19 2024 +#> Added naiveBayes predictions according to global threshold = 1 Sun Jun 16 09:18:19 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "naiveBayes") @@ -237,15 +236,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/nbOptimisation.html b/reference/nbOptimisation.html index eb2233a6..c7aec1be 100644 --- a/reference/nbOptimisation.html +++ b/reference/nbOptimisation.html @@ -1,9 +1,9 @@ -nb paramter optimisation — nbOptimisation • pRolocnb paramter optimisation — nbOptimisation • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -91,53 +91,53 @@

    nb paramter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    laplace
    +
    laplace

    The hyper-parameter. Default values are seq(0, 5, 0.5).

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to naiveBayes from package e1071.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -170,15 +170,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/nicheMeans2D.html b/reference/nicheMeans2D.html index 27fa66f5..b0ea9ac6 100644 --- a/reference/nicheMeans2D.html +++ b/reference/nicheMeans2D.html @@ -1,10 +1,10 @@ -Uncertainty plot organelle means — nicheMeans2D • pRolocUncertainty plot organelle means — nicheMeans2D • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -89,37 +89,37 @@

    Uncertainty plot organelle means

    Arguments

    -
    object
    + + +
    object

    A valid object of class MSnset

    -
    params
    +
    params

    A valid object of class MCMCParams that has been processed and checked for convergence

    -
    priors
    +
    priors

    The prior that were used in the model

    -
    dims
    +
    dims

    The PCA dimension in which to project he data, default is c(1,2)

    -
    fcol
    +
    fcol

    The columns of the feature data which contain the marker data.

    -
    aspect
    +
    aspect

    A argument to change the plotting aspect of the PCA

    Value

    - - -

    Used for side effect of producing plot. Invisibily returns an ggplot +

    Used for side effect of producing plot. Invisibily returns an ggplot object that can be further manipulated

    @@ -129,7 +129,7 @@

    Author

    Examples

    -
    if (FALSE) {
    +    
    if (FALSE) { # \dontrun{
     library("pRolocdata")
     data("tan2009r1")
     
    @@ -140,7 +140,7 @@ 

    Examples

    myparams2 <- chains(mcmc_pool_chains(tanres))[[1]] priors <- tanres@priors pRoloc:::nicheMeans2D(object = tan2009r1, params = myparams2, priors = priors) -} +} # }
    @@ -155,15 +155,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/nndist-methods.html b/reference/nndist-methods.html index 20c9f54d..ef2d9697 100644 --- a/reference/nndist-methods.html +++ b/reference/nndist-methods.html @@ -1,10 +1,10 @@ -Nearest neighbour distances — nndist-methods • pRolocNearest neighbour distances — nndist-methods • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -79,7 +79,7 @@

    Nearest neighbour distances

    Methods

    - +

    signature(object = "matrix", k = "numeric", dist = "character", ...)
    @@ -98,7 +98,7 @@

    Methods

    input. The indices and distances to the k nearest neighbours are added to the object's feature metadata.

    - +
    signature(object = "matrix", query = "matrix", k = "numeric", ...)

    If two matrix instances are provided as @@ -109,7 +109,7 @@

    Methods

    FNN::get.knnx. Only euclidean distance is available.

    - +
    @@ -218,15 +218,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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zc>B+O(MZc*S!63?L-g0(j!!|{{r~5ekI2dq`GlRDeg1QUa^izR -nnet classification — nnetClassification • pRolocnnet classification — nnetClassification • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -90,49 +90,48 @@

    nnet classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by nnetOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    decay
    +
    decay

    If assessRes is missing, a decay must be provided.

    -
    size
    +
    size

    If assessRes is missing, a size must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to nnet from package nnet.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    nnet and nnet.scores feature variables storing +

    An instance of class "MSnSet" with + nnet and nnet.scores feature variables storing the classification results and scores respectively.

    @@ -159,42 +158,41 @@

    Examples

    #> Results #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> 0.7394 0.8434 0.9473 0.8956 0.9736 1.0000 -#> best decay: 1e-05 0.1 -#> best size: 5 10 +#> 0.9500 0.9681 0.9862 0.9787 0.9931 1.0000 +#> best decay: 1e-05 +#> best size: 10 plot(params) f1Count(params) -#> 5 10 -#> 1e-05 0 0 -#> 0.1 NA 1 +#> 10 +#> 1e-05 1 levelPlot(params) getParams(params) #> decay size -#> 0.1 10.0 +#> 1e-05 1e+01 res <- nnetClassification(dunkley2006, params) #> [1] "markers" #> # weights: 269 -#> initial value 683.283502 -#> iter 10 value 389.436994 -#> iter 20 value 249.736502 -#> iter 30 value 178.340493 -#> iter 40 value 165.325969 -#> iter 50 value 160.469658 -#> iter 60 value 158.052692 -#> iter 70 value 157.274930 -#> iter 80 value 156.907896 -#> iter 90 value 156.721932 -#> iter 100 value 156.674206 -#> final value 156.674206 +#> initial value 717.879821 +#> iter 10 value 304.725847 +#> iter 20 value 111.542780 +#> iter 30 value 14.757886 +#> iter 40 value 4.279708 +#> iter 50 value 2.339038 +#> iter 60 value 1.240553 +#> iter 70 value 0.892605 +#> iter 80 value 0.810195 +#> iter 90 value 0.729111 +#> iter 100 value 0.697179 +#> final value 0.697179 #> stopped after 100 iterations getPredictions(res, fcol = "nnet") #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 18 187 93 106 130 +#> 19 176 103 104 123 #> Plastid Ribosome TGN vacuole -#> 49 51 21 34 +#> 54 59 20 31 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -214,15 +212,15 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed nnet prediction (decay=0.1 size=10) Tue Mar 12 07:14:04 2024 -#> Added nnet predictions according to global threshold = 0 Tue Mar 12 07:14:04 2024 +#> Performed nnet prediction (decay=1e-05 size=10) Sun Jun 16 09:18:24 2024 +#> Added nnet predictions according to global threshold = 0 Sun Jun 16 09:18:24 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "nnet", t = 0.75) #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 14 148 68 93 93 +#> 19 176 102 104 118 #> Plastid Ribosome TGN unknown vacuole -#> 41 31 13 160 28 +#> 52 56 18 13 31 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -242,8 +240,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed nnet prediction (decay=0.1 size=10) Tue Mar 12 07:14:04 2024 -#> Added nnet predictions according to global threshold = 0.75 Tue Mar 12 07:14:04 2024 +#> Performed nnet prediction (decay=1e-05 size=10) Sun Jun 16 09:18:24 2024 +#> Added nnet predictions according to global threshold = 0.75 Sun Jun 16 09:18:24 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "nnet") @@ -261,15 +259,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/nnetOptimisation.html b/reference/nnetOptimisation.html index 866b427d..9959ba4e 100644 --- a/reference/nnetOptimisation.html +++ b/reference/nnetOptimisation.html @@ -1,10 +1,10 @@ -nnet parameter optimisation — nnetOptimisation • pRolocnnet parameter optimisation — nnetOptimisation • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -94,57 +94,57 @@

    nnet parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    decay
    +
    decay

    The hyper-parameter. Default values are c(0, 10^(-1:-5)).

    -
    size
    +
    size

    The hyper-parameter. Default values are seq(1, 10, 2).

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to nnet from package nnet.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -177,15 +177,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/orderGoAnnotations.html b/reference/orderGoAnnotations.html index fca0bd54..865d129a 100644 --- a/reference/orderGoAnnotations.html +++ b/reference/orderGoAnnotations.html @@ -1,11 +1,11 @@ -Orders annotation information — orderGoAnnotations • pRolocOrders annotation information — orderGoAnnotations • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    -

    For a given matrix of annotation information, -this function returns the information ordered according to +

    For a given matrix of annotation information, +this function returns the information ordered according to the best fit with the data.

    @@ -92,65 +92,65 @@

    Orders annotation information

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    The name of the annotations matrix. Default is GOAnnotations.

    -
    k
    +
    k

    The number of clusters to test. Default is k = 1:5

    -
    n
    +
    n

    The minimum number of proteins per component cluster.

    -
    p
    +
    p

    The normalisation factor, per k tested

    -
    verbose
    +
    verbose

    A logical indicating if a progress bar should be displayed. Default is TRUE.

    -
    seed
    +
    seed

    An optional random number generation seed.

    Value

    - - -

    An updated MSnSet containing the newly ordered +

    An updated MSnSet containing the newly ordered fcol matrix.

    Details

    As there are typically many protein/annotation sets that may fit the data we order protein sets by best fit i.e. cluster tightness, by -computing the mean normalised Euclidean distance for all instances +computing the mean normalised Euclidean distance for all instances per protein set.

    For each protein set i.e. proteins that have been labelled -with a specified term/information criteria, we find the best -k cluster components for the set (the default is to -testk = 1:5) according to the minimum mean normalised -pairwise Euclidean distance over all component clusters. -(Note: when testing k if any components are found to +with a specified term/information criteria, we find the best +k cluster components for the set (the default is to +testk = 1:5) according to the minimum mean normalised +pairwise Euclidean distance over all component clusters. +(Note: when testing k if any components are found to have less than n proteins these components are not included and k is reduced by 1).

    -

    Each component cluster is normalised by N^p (where -N is the total number of proteins per component, -and p is the power). Hueristally, p = 1/3 -and normalising by N^1/3 has been found the optimum +

    Each component cluster is normalised by N^p (where +N is the total number of proteins per component, +and p is the power). Hueristally, p = 1/3 +and normalising by N^1/3 has been found the optimum normalisation factor.

    -

    Candidates in the matrix are ordered according to lowest -mean normalised pairwise Euclidean distance as we expect -high density, tight clusters to have the smallest mean +

    Candidates in the matrix are ordered according to lowest +mean normalised pairwise Euclidean distance as we expect +high density, tight clusters to have the smallest mean normalised distance.

    This function is a wrapper for running clustDist, getNormDist, see the "Annotating spatial proteomics data" @@ -177,15 +177,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/orgQuants.html b/reference/orgQuants.html index 07e6f21d..79034911 100644 --- a/reference/orgQuants.html +++ b/reference/orgQuants.html @@ -1,10 +1,10 @@ -Returns organelle-specific quantile scores — orgQuants • pRolocReturns organelle-specific quantile scores — orgQuants • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    -

    This function produces organelle-specific quantiles corresponding to +

    This function produces organelle-specific quantiles corresponding to the given classification scores.

    @@ -82,39 +82,39 @@

    Returns organelle-specific quantile scores

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The name of the prediction column in the featureData slot.

    -
    scol
    +
    scol

    The name of the prediction score column in the featureData slot. If missing, created by pasting '.scores' after fcol.

    -
    mcol
    +
    mcol

    The name of the column containing the training data in the featureData slot. Default is markers.

    -
    t
    +
    t

    The quantile threshold.

    -
    verbose
    +
    verbose

    If TRUE, the calculated threholds are printed.

    Value

    - - -

    A named vector of organelle thresholds.

    +

    A named vector of organelle thresholds.

    See also

    @@ -136,15 +136,15 @@

    Examples

    ## 50% top predictions per class ts <- orgQuants(res, fcol = "svm", t = .5) #> ER lumen ER membrane Golgi Mitochondrion PM -#> 0.3129695 0.8380783 0.7755526 0.7423607 0.7356209 +#> 0.3061708 0.8405322 0.7772791 0.7522059 0.7316163 #> Plastid Ribosome TGN vacuole -#> 0.7683069 0.5552199 0.5629277 0.5697818 +#> 0.7736651 0.5292914 0.5527200 0.5632360 getPredictions(res, fcol = "svm", t = ts) #> ans #> ER lumen ER membrane Golgi Mitochondrion PM #> 15 117 65 78 86 #> Plastid Ribosome TGN unknown vacuole -#> 37 40 15 211 25 +#> 36 39 15 212 26 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -164,8 +164,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed svm prediction (cost=0.5 sigma=0.1) Tue Mar 12 07:14:05 2024 -#> Added svm predictions according to thresholds: ER lumen = 0.31, ER membrane = 0.84, Golgi = 0.78, Mitochondrion = 0.74, PM = 0.74, Plastid = 0.77, Ribosome = 0.56, TGN = 0.56, vacuole = 0.57 Tue Mar 12 07:14:05 2024 +#> Performed svm prediction (cost=0.5 sigma=0.1) Sun Jun 16 09:18:25 2024 +#> Added svm predictions according to thresholds: ER lumen = 0.31, ER membrane = 0.84, Golgi = 0.78, Mitochondrion = 0.75, PM = 0.73, Plastid = 0.77, Ribosome = 0.53, TGN = 0.55, vacuole = 0.56 Sun Jun 16 09:18:25 2024 #> MSnbase version: 1.17.12
    @@ -181,15 +181,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/pRolocmarkers.html b/reference/pRolocmarkers.html index 49362a46..afba2fc9 100644 --- a/reference/pRolocmarkers.html +++ b/reference/pRolocmarkers.html @@ -1,12 +1,12 @@ -Organelle markers — pRolocmarkers • pRolocOrganelle markers — pRolocmarkers • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    @@ -86,15 +86,15 @@

    Organelle markers

    Arguments

    -
    species
    + + +
    species

    The species of interest.

    Value

    - - -

    Prints a description of the available marker lists if +

    Prints a description of the available marker lists if species is missing or a named character with organelle markers.

    @@ -176,15 +176,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    @@ -28,7 +28,7 @@
    - +
    @@ -89,35 +89,37 @@

    perTurbo classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by svmRegularisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    pRegul
    +
    pRegul

    If assessRes is missing, a pRegul must be provided. See perTurboOptimisation for details.

    -
    sigma
    +
    sigma

    If assessRes is missing, a sigma must be provided. See perTurboOptimisation for details.

    -
    inv
    +
    inv

    The type of algorithm used to invert the matrix. Values are : "Inversion Cholesky" (chol2inv), "Moore Penrose" (ginv), "solve" @@ -125,24 +127,21 @@

    Arguments

    value is "Inversion Cholesky".

    -
    reg
    +
    reg

    The type of regularisation of matrix. Values are "none", "trunc" or "tikhonov". Default value is "tikhonov".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    perTurbo and perTurbo.scores feature variables +

    An instance of class "MSnSet" with + perTurbo and perTurbo.scores feature variables storing the classification results and scores respectively.

    @@ -185,25 +184,26 @@

    Examples

    #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 1 1 1 1 1 1 -#> best sigma: 0.1 -#> best pRegul: 4 1 +#> best sigma: 1 0.1 +#> best pRegul: 0.25 1 plot(params) f1Count(params) -#> 1 4 -#> 0.1 1 2 +#> 0.25 1 +#> 0.1 NA 1 +#> 1 2 NA levelPlot(params) getParams(params) #> sigma pRegul -#> 0.1 4.0 +#> 1.00 0.25 res <- perTurboClassification(dunkley2006, params) getPredictions(res, fcol = "perTurbo") #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 20 181 96 107 134 +#> 17 184 96 106 133 #> Plastid Ribosome TGN vacuole -#> 49 50 20 32 +#> 49 51 19 34 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -223,15 +223,15 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed perTurbo prediction (sigma=0.1 pRegul=4) Tue Mar 12 07:14:18 2024 -#> Added perTurbo predictions according to global threshold = 0 Tue Mar 12 07:14:18 2024 +#> Performed perTurbo prediction (sigma=1 pRegul=0.25) Sun Jun 16 09:18:35 2024 +#> Added perTurbo predictions according to global threshold = 0 Sun Jun 16 09:18:35 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "perTurbo", t = 0.75) #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 14 45 28 55 46 -#> Plastid Ribosome TGN unknown vacuole -#> 20 19 13 428 21 +#> 17 184 96 106 133 +#> Plastid Ribosome TGN vacuole +#> 49 51 19 34 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -251,8 +251,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed perTurbo prediction (sigma=0.1 pRegul=4) Tue Mar 12 07:14:18 2024 -#> Added perTurbo predictions according to global threshold = 0.75 Tue Mar 12 07:14:18 2024 +#> Performed perTurbo prediction (sigma=1 pRegul=0.25) Sun Jun 16 09:18:35 2024 +#> Added perTurbo predictions according to global threshold = 0.75 Sun Jun 16 09:18:35 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "perTurbo") @@ -270,15 +270,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/perTurboOptimisation.html b/reference/perTurboOptimisation.html index 11ab1798..425fd918 100644 --- a/reference/perTurboOptimisation.html +++ b/reference/perTurboOptimisation.html @@ -1,9 +1,9 @@ -PerTurbo parameter optimisation — perTurboOptimisation • pRolocPerTurbo parameter optimisation — perTurboOptimisation • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -93,16 +93,18 @@

    PerTurbo parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    pRegul
    +
    pRegul

    The hyper-parameter for the regularisation (values are in ]0,1] ). If reg =="trunc", pRegul is for the percentage of eigen values in matrix. If reg =="tikhonov", then 'pRegul' is the parameter for the tikhonov regularisation. @@ -113,11 +115,11 @@

    Arguments

    "svd" - ("tikhonov" / "none" / "trunc").

    -
    sigma
    +
    sigma

    The hyper-parameter.

    -
    inv
    +
    inv

    The type of algorithm used to invert the matrix. Values are : "Inversion Cholesky" (chol2inv), @@ -127,42 +129,40 @@

    Arguments

    Default value is "Inversion Cholesky".

    -
    reg
    +
    reg

    The type of regularisation of matrix. Values are "none", "trunc" or "tikhonov". Default value is "tikhonov".

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the times macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -195,15 +195,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/phenoDisco.html b/reference/phenoDisco.html index 5bd6ff23..1fbb9038 100644 --- a/reference/phenoDisco.html +++ b/reference/phenoDisco.html @@ -1,10 +1,10 @@ -Runs the phenoDisco algorithm. — phenoDisco • pRolocRuns the phenoDisco algorithm. — phenoDisco • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -98,40 +98,42 @@

    Runs the phenoDisco algorithm.

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    A character indicating the organellar markers column name in feature meta-data. Default is markers.

    -
    times
    +
    times

    Number of runs of tracking. Default is 100.

    -
    GS
    +
    GS

    Group size, i.e how many proteins make a group. Default is 10 (the minimum group size is 4).

    -
    allIter
    +
    allIter

    logical, defining if predictions for all iterations should be saved. Default is FALSE.

    -
    p
    +
    p

    Significance level for outlier detection. Default is 0.05.

    -
    ndims
    +
    ndims

    Number of principal components to use as input for the disocvery analysis. Default is 2. Added in version 1.3.9.

    -
    modelNames
    +
    modelNames

    A vector of characters indicating the models to be fitted in the EM phase of clustering using Mclust. The help file for mclust::mclustModelNames @@ -144,13 +146,13 @@

    Arguments

    i.e. only ellipsoidal models.

    -
    G
    +
    G

    An integer vector specifying the numbers of mixture components (clusters) for which the BIC is to be calculated. The default is G=1:9 (as in Mclust).

    -
    BPPARAM
    +
    BPPARAM

    Support for parallel processing using the BiocParallel infrastructure. When missing (default), the default registered BiocParallelParam parameters are @@ -161,7 +163,7 @@

    Arguments

    origianl serial implementation, use NULL.

    -
    tmpfile
    +
    tmpfile

    An optional character to save a temporary MSnSet after each iteration. Ignored if missing. This is useful for long runs to track phenotypes and possibly kill @@ -170,26 +172,26 @@

    Arguments

    result.

    -
    seed
    +
    seed

    An optional numeric of length 1 specifing the random number generator seed to be used. Only relevant when executed in serialised mode with BPPARAM = NULL. See BPPARAM for details.

    -
    verbose
    +
    verbose

    Logical, indicating if messages are to be printed out during execution of the algorithm.

    -
    dimred
    +
    dimred

    A characater defining which of Principal Component Analysis ("PCA") or t-Distributed Stochastic Neighbour Embedding ("t-SNE") should be use to reduce dimensions prior to running phenoDisco novelty detection.

    -
    ...
    +
    ...

    Additional arguments passed to the dimensionality reduction method. For both PCA and t-SNE, the data is scaled and centred by default, and these parameters (scale and @@ -202,11 +204,8 @@

    Arguments

    Value

    - - -

    An instance of class MSnSet containing the

    -

    -

    phenoDisco predictions.

    +

    An instance of class MSnSet containing the + phenoDisco predictions.

    Details

    @@ -249,7 +248,7 @@

    Author

    Examples

    -
    if (FALSE) {
    +    
    if (FALSE) { # \dontrun{
     library(pRolocdata)
     data(tan2009r1)
     pdres <- phenoDisco(tan2009r1, fcol = "PLSDA")
    @@ -258,7 +257,7 @@ 

    Examples

    ## to pre-process the data with t-SNE instead of PCA pdres <- phenoDisco(tan2009r1, fcol = "PLSDA", dimred = "t-SNE") -} +} # }
    @@ -273,15 +272,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/plot2D.html b/reference/plot2D.html index dd8c439f..a95befe8 100644 --- a/reference/plot2D.html +++ b/reference/plot2D.html @@ -1,5 +1,5 @@ -Plot organelle assignment data and results. — plot2D • pRoloc - +
    @@ -33,7 +33,7 @@
    - +
    @@ -109,7 +109,7 @@

    Plot organelle assignment data and results.

    ... ) -# S4 method for MSnSet +# S4 method for class 'MSnSet' plot3D( object, fcol = "markers", @@ -123,41 +123,43 @@

    Plot organelle assignment data and results.

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is markers. Use NULL to suppress any colouring.

    -
    fpch
    +
    fpch

    Featre meta-data label (fData column name) desining the groups to be differentiated using different point symbols.

    -
    unknown
    +
    unknown

    A character (default is "unknown") defining how proteins of unknown/un-labelled localisation are labelled.

    -
    dims
    +
    dims

    A numeric of length 2 (or 3 for plot3D) defining the dimensions to be plotted. Defaults are c(1, 2) and c(1, 2, 3). Always 1:2 for MDS.

    -
    score
    +
    score

    A numeric specifying the minimum organelle assignment score to consider features to be assigned an organelle. (not yet implemented).

    -
    method
    +
    method

    A character describe how to transform the data or what to plot. One of "PCA" (default), "MDS", "kpca", "nipals", "t-SNE" @@ -193,7 +195,7 @@

    Arguments

    Available methods are listed in plot2Dmethods.

    -
    methargs
    +
    methargs

    A list of arguments to be passed when method is called. If missing, the data will be scaled and centred prior to PCA and t-SNE (i.e. Rtsne's @@ -204,91 +206,89 @@

    Arguments

    features with object.

    -
    axsSwitch
    +
    axsSwitch

    A logical indicating whether the axes should be switched.

    -
    mirrorX
    +
    mirrorX

    A logical indicating whether the x axis should be mirrored?

    -
    mirrorY
    +
    mirrorY

    A logical indicating whether the y axis should be mirrored?

    -
    col
    +
    col

    A character of appropriate length defining colours.

    -
    pch
    +
    pch

    A character of appropriate length defining point character.

    -
    cex
    +
    cex

    Character expansion.

    -
    index
    +
    index

    A logical (default is FALSE, indicating of the feature indices should be plotted on top of the symbols.

    -
    idx.cex
    +
    idx.cex

    A numeric specifying the character expansion (default is 0.75) for the feature indices. Only relevant when index is TRUE.

    -
    addLegend
    +
    addLegend

    A character indicating where to add the legend. See addLegendfor details. If missing (default), no legend is added.

    -
    identify
    +
    identify

    A logical (default is TRUE) defining if user interaction will be expected to identify individual data points on the plot. See also identify.

    -
    plot
    +
    plot

    A logical defining if the figure should be plotted. Useful when retrieving data only. Default is TRUE.

    -
    grid
    +
    grid

    A logical indicating whether a grid should be plotted. Default is TRUE.

    -
    ...
    +
    ...

    Additional parameters passed to plot and points.

    -
    radius1
    +
    radius1

    A numeric specifying the radius of feature of unknown localisation. Default is 0.1, which is specidied on the data scale. See plot3d for details.

    -
    radius2
    +
    radius2

    A numeric specifying the radius of marker feature. Default is radius * 2.

    Value

    - - -

    Used for its side effects of generating a plot. Invisibly +

    Used for its side effects of generating a plot. Invisibly returns the 2 or 3 dimensions that are plotted.

    @@ -412,15 +412,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/plot2Ds.html b/reference/plot2Ds.html index 1b43a616..77886aa8 100644 --- a/reference/plot2Ds.html +++ b/reference/plot2Ds.html @@ -1,12 +1,12 @@ -Draw 2 data sets on one PCA plot — plot2Ds • pRolocDraw 2 data sets on one PCA plot — plot2Ds • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    @@ -99,14 +99,16 @@

    Draw 2 data sets on one PCA plot

    Arguments

    -
    object
    + + +
    object

    An MSnSet or a MSnSetList. In the latter case, only the two first elements of the list will be used for plotting and the others will be silently ignored.

    -
    pcol
    +
    pcol

    If object is an MSnSet, a factor or the name of a phenotype variable (phenoData slot) defining how to split the single MSnSet into two or @@ -114,73 +116,68 @@

    Arguments

    MSnSetList.

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. Default is markers. Use NULL to suppress any colouring.

    -
    cex.x
    +
    cex.x

    Character expansion for the first data set. Default is 1.

    -
    cex.y
    +
    cex.y

    Character expansion for the second data set. Default is 1.

    -
    pch.x
    +
    pch.x

    Plotting character for the first data set. Default is 21.

    -
    pch.y
    +
    pch.y

    Plotting character for the second data set. Default is 23.

    -
    col
    +
    col

    A vector of colours to highlight the different classes defined by fcol. If missing (default), default colours are used (see getStockcol).

    -
    mirrorX
    +
    mirrorX

    A logical indicating whether the x axis should be mirrored?

    -
    mirrorY
    +
    mirrorY

    A logical indicating whether the y axis should be mirrored?

    -
    plot
    +
    plot

    If TRUE (default), a plot is produced.

    -
    ...
    +
    ...

    Additinal parameters passed to plot and points.

    Value

    - - -

    Used for its side effects of producing a plot. Invisibly +

    Used for its side effects of producing a plot. Invisibly returns an object of class plot2Ds, which is a list with the PCA analyses results (see prcomp) of the first data set and the new coordinates of the second data sets, as used to produce the plot and the respective point - colours. Each of these elements can be accessed with

    -

    -

    data1, data2, col1 and code2

    - - -

    respectively.

    + colours. Each of these elements can be accessed with + data1, data2, col1 and code2 + respectively.

    See also

    @@ -1879,15 +1876,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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+1,9 @@ -Plot marker consenses profiles. — plotConsProfiles • pRolocPlot marker consenses profiles. — plotConsProfiles • pRoloc - +

    @@ -28,7 +28,7 @@
    - +
    @@ -80,25 +80,25 @@

    Plot marker consenses profiles.

    Arguments

    -
    object
    + + +
    object

    A matrix containing marker consensus profiles as output from mrkConsProfiles().

    -
    order
    +
    order

    Order for markers (optional).

    -
    plot
    +
    plot

    A logical(1) defining whether the heatmap should be plotted. Default is TRUE.

    Value

    - - -

    Invisibly returns ggplot2 object.

    +

    Invisibly returns ggplot2 object.

    Author

    @@ -129,15 +129,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/plotDist.html b/reference/plotDist.html index ebe06200..6c7ab765 100644 --- a/reference/plotDist.html +++ b/reference/plotDist.html @@ -1,10 +1,10 @@ -Plots the distribution of features across fractions — plotDist • pRolocPlots the distribution of features across fractions — plotDist • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -96,80 +96,80 @@

    Plots the distribution of features across fractions

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    markers
    +
    markers

    A character, numeric or logical of appropriate length and or content used to subset object and define the organelle markers.

    -
    fcol
    +
    fcol

    Feature meta-data label (fData column name) defining the groups to be differentiated using different colours. If NULL (default) ignored and mcol and pcol are used.

    -
    mcol
    +
    mcol

    A character define the colour of the marker features. Default is "steelblue".

    -
    pcol
    +
    pcol

    A character define the colour of the non-markers features. Default is the colour used for features of unknown localisation, as returned by getUnknowncol.

    -
    alpha
    +
    alpha

    A numeric defining the alpha channel (transparency) of the points, where 0 <= alpha <= 1, 0 and 1 being completely transparent and opaque.

    -
    type
    +
    type

    Character string defining the type of lines. For example "p" for points, "l" for lines, "b" for both. See plot for all possible types.

    -
    lty
    +
    lty

    Vector of line types for the marker profiles. Default is 1 (solid). See par for details.

    -
    fractions
    +
    fractions

    A character defining the phenoData variable to be used to label the fraction along the x axis. Default is to use sampleNames(object).

    -
    ylab
    +
    ylab

    y-axis label. Default is "Intensity".

    -
    xlab
    +
    xlab

    x-axis label. Default is "Fractions".

    -
    ylim
    +
    ylim

    A numeric vector of length 2, giving the y coordinates range.

    -
    ...
    +
    ...

    Additional parameters passed to plot.

    Value

    - - -

    Used for its side effect of producing a feature +

    Used for its side effect of producing a feature distribution plot. Invisibly returns the data matrix.

    @@ -206,15 +206,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/plotEllipse.html b/reference/plotEllipse.html index e5484f09..a8c99adb 100644 --- a/reference/plotEllipse.html +++ b/reference/plotEllipse.html @@ -1,12 +1,12 @@ -A function to plot probabiltiy ellipses on marker PCA plots to visualise and assess TAGM models. — plotEllipse • pRolocA function to plot probabiltiy ellipses on marker PCA plots to visualise and assess TAGM models. — plotEllipse • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    @@ -86,44 +86,38 @@

    A function to plot probabiltiy ellipses on marker PCA plots to visualise and

    Arguments

    -
    object
    + + +
    object

    An [`MSnbase::MSnset`] containing quantitative spatial proteomics data.

    -
    params
    +
    params

    An [`MAPParams`] with the TAGM-MAP parameters, as generated by `tagmMapTrain`.

    -
    dims
    +
    dims

    A `numeric(2)` with the principal components along which to project the data. Default is `c(1, 2)`.

    -
    method
    +
    method

    The method used. Currently `"MAP"` only.

    -
    ...
    +
    ...

    Additional parameters passed to [plot2D()].

    Value

    - - -

    A PCA plot of the marker data with probability - ellipises. The outer ellipse contains 99

    - - -

    probability whilst the middle and inner ellipses contain 95

    - - -

    and 90

    - - -

    clusters are represented by black circumpunct (circled dot).

    +

    A PCA plot of the marker data with probability + ellipises. The outer ellipse contains 99 + probability whilst the middle and inner ellipses contain 95 + and 90 + clusters are represented by black circumpunct (circled dot).

    See also

    @@ -145,15 +139,15 @@

    See also

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/plsdaClassification.html b/reference/plsdaClassification.html index f765a112..3686caea 100644 --- a/reference/plsdaClassification.html +++ b/reference/plsdaClassification.html @@ -1,10 +1,10 @@ -plsda classification — plsdaClassification • pRolocplsda classification — plsdaClassification • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -89,44 +89,43 @@

    plsda classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by plsdaOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    ncomp
    +
    ncomp

    If assessRes is missing, a ncomp must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to plsda from package caret.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    plsda and plsda.scores feature variables storing +

    An instance of class "MSnSet" with + plsda and plsda.scores feature variables storing the classification results and scores respectively.

    @@ -151,8 +150,8 @@

    Examples

    #> default = NULL, skeleton = (function (x, ...) #> stop(gettextf("invalid call in method dispatch to '%s' (no default method)", #> "params"), domain = NA))(x, ...)) -#> <bytecode: 0x561e88e96250> -#> <environment: 0x561e88e90a68> +#> <bytecode: 0x559d64cfeac8> +#> <environment: 0x559d64d06dd8> #> attr(,"generic") #> [1] "params" #> attr(,"generic")attr(,"package") @@ -206,15 +205,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/plsdaOptimisation.html b/reference/plsdaOptimisation.html index 0297d6ab..137ea31e 100644 --- a/reference/plsdaOptimisation.html +++ b/reference/plsdaOptimisation.html @@ -1,10 +1,10 @@ -plsda parameter optimisation — plsdaOptimisation • pRolocplsda parameter optimisation — plsdaOptimisation • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -93,53 +93,53 @@

    plsda parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ncomp
    +
    ncomp

    The hyper-parameter. Default values are 2:6.

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to plsda from package caret.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -172,15 +172,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    Zrz z^b9GqIBnDX!aAKcl4I@I7D-)&6(>@ff*rTu6);E8u^?qu*c;QYH+JM7Y6Vl#@!zJp zUxYyb%U=`>{A@a)i?IlP&qc)(>F;K8%mdb~IRA6kwmb6IxHM7jxvUacPc;i7GaMF) z!1`&Zn3jBtC}6T)8>C2T zh18pO-w|4u_6;A_kN2djPWw{nN4yQBqwyMsc*DT3GkG3GVo6#--uECK74|i`Mc=MsyanYgL ztLt8VSkPQ*1uN2h%(wnq(>+v-9o@hG+WSQEw8AnZAvw9}9(F@-Dy_Ac(vq*m7D~c) zddPiqr`XeCe$EfyN`sRye<58QxYKEF<0?N)iX6DJ@mi^M`Xx3+0>Xi`i92a-8V<4G ztGN>!e_VTfsy$Q{0z_zk%}4-|}MT pGOb>~wrp(iN&Ptbn?GNs_ -rf classification — rfClassification • pRolocrf classification — rfClassification • pRoloc - +

    @@ -28,7 +28,7 @@
    - +
    @@ -87,44 +87,43 @@

    rf classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by rfOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    mtry
    +
    mtry

    If assessRes is missing, a mtry must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to randomForest from package randomForest.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    rf and rf.scores feature variables storing the +

    An instance of class "MSnSet" with + rf and rf.scores feature variables storing the classification results and scores respectively.

    @@ -150,7 +149,7 @@

    Examples

    #> Results #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> 0.9788 0.9817 0.9846 0.9878 0.9923 1.0000 +#> 0.9288 0.9553 0.9818 0.9702 0.9909 1.0000 #> best mtry: 2 5 plot(params) @@ -168,9 +167,9 @@

    Examples

    getPredictions(res, fcol = "rf") #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 19 177 95 102 135 +#> 19 177 96 101 135 #> Plastid Ribosome TGN vacuole -#> 54 52 22 33 +#> 54 51 21 34 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -190,15 +189,15 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed random forest prediction (mtry=5) Tue Mar 12 07:14:32 2024 -#> Added rf predictions according to global threshold = 0 Tue Mar 12 07:14:32 2024 +#> Performed random forest prediction (mtry=5) Sun Jun 16 09:18:49 2024 +#> Added rf predictions according to global threshold = 0 Sun Jun 16 09:18:49 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "rf", t = 0.75) #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 14 145 78 91 96 +#> 14 148 78 90 94 #> Plastid Ribosome TGN unknown vacuole -#> 47 21 13 157 27 +#> 46 22 13 157 27 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -218,8 +217,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed random forest prediction (mtry=5) Tue Mar 12 07:14:32 2024 -#> Added rf predictions according to global threshold = 0.75 Tue Mar 12 07:14:32 2024 +#> Performed random forest prediction (mtry=5) Sun Jun 16 09:18:49 2024 +#> Added rf predictions according to global threshold = 0.75 Sun Jun 16 09:18:49 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "rf") @@ -237,15 +236,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/rfOptimisation.html b/reference/rfOptimisation.html index cf21122f..b7617c36 100644 --- a/reference/rfOptimisation.html +++ b/reference/rfOptimisation.html @@ -1,9 +1,9 @@ -svm parameter optimisation — rfOptimisation • pRolocsvm parameter optimisation — rfOptimisation • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -91,53 +91,53 @@

    svm parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    mtry
    +
    mtry

    The hyper-parameter. Default value is NULL.

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to randomForest from package randomForest.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -170,15 +170,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/sampleMSnSet.html b/reference/sampleMSnSet.html index 50a49ffd..ece7ce2b 100644 --- a/reference/sampleMSnSet.html +++ b/reference/sampleMSnSet.html @@ -1,9 +1,9 @@ -Extract a stratified sample of an MSnSet — sampleMSnSet • pRolocExtract a stratified sample of an MSnSet — sampleMSnSet • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -80,30 +80,30 @@

    Extract a stratified sample of an MSnSet

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet

    -
    fcol
    +
    fcol

    The feature meta-data column name containing the marker (vector or matrix) definitions on which the MSnSet will be stratified. Default is markers.

    -
    size
    +
    size

    The size of the stratified sample to be extracted. Default is 0.2 (20 percent).

    -
    seed
    +
    seed

    The optional random number generator seed.

    Value

    - - -

    A stratified sample (according to the defined fcol) +

    A stratified sample (according to the defined fcol) which is an instance of class "MSnSet".

    @@ -157,15 +157,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/showGOEvidenceCodes.html b/reference/showGOEvidenceCodes.html index 7c1431f9..2be57937 100644 --- a/reference/showGOEvidenceCodes.html +++ b/reference/showGOEvidenceCodes.html @@ -1,10 +1,10 @@ -GO Evidence Codes — showGOEvidenceCodes • pRolocGO Evidence Codes — showGOEvidenceCodes • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -84,9 +84,7 @@

    GO Evidence Codes

    Value

    - - -

    These functions are used for their side effects of printing +

    These functions are used for their side effects of printing evidence codes and their description.

    @@ -143,15 +141,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/spatial2D.html b/reference/spatial2D.html index f5119a34..2b811ec1 100644 --- a/reference/spatial2D.html +++ b/reference/spatial2D.html @@ -1,9 +1,9 @@ -Uncertainty plot in localisation probabilities — spatial2D • pRolocUncertainty plot in localisation probabilities — spatial2D • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -89,50 +89,50 @@

    Uncertainty plot in localisation probabilities

    Arguments

    -
    object
    + + +
    object

    A valid object of class MSnset with mcmc prediction results from tagmMCMCpredict

    -
    dims
    +
    dims

    The PCA dimension in which to project he data, default is c(1,2)

    -
    cov.function
    +
    cov.function

    The covariance function used default is wendland.cov. See fields package.

    -
    theta
    +
    theta

    A hyperparameter to the covariance function. See fields package. Default is 1.

    -
    derivative
    +
    derivative

    The number of derivative of the wendland kernel. See fields package. Default is 2.

    -
    k
    +
    k

    A hyperparamter to the covariance function. See fields package. Default is 1.

    -
    breaks
    +
    breaks

    Probability values at which to draw the contour bands. Default is c(0.99, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7)

    -
    aspect
    +
    aspect

    A argument to change the plotting aspect of the PCA

    Value

    - - -

    Used for side effect of producing plot. Invisibily returns an ggplot +

    Used for side effect of producing plot. Invisibily returns an ggplot object that can be further manipulated

    @@ -142,7 +142,7 @@

    Author

    Examples

    -
    if (FALSE) {
    +    
    if (FALSE) { # \dontrun{
     library("pRolocdata")
     data("tan2009r1")
     
    @@ -150,7 +150,7 @@ 

    Examples

    tanres <- tagmMcmcProcess(tanres) tan2009r1 <- tagmMcmcPredict(object = tan2009r1, params = tanres, probJoint = TRUE) spatial2D(object = tan2009r1) -} +} # }
    @@ -165,15 +165,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/subsetMarkers.html b/reference/subsetMarkers.html index 6aa2b219..c0f7b33f 100644 --- a/reference/subsetMarkers.html +++ b/reference/subsetMarkers.html @@ -1,9 +1,9 @@ -Subsets markers — subsetMarkers • pRolocSubsets markers — subsetMarkers • pRoloc - +
    @@ -28,7 +28,7 @@
    - +
    @@ -80,27 +80,25 @@

    Subsets markers

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet.

    -
    fcol
    +
    fcol

    The name of the markers matrix. Default is GOAnnotations.

    -
    keep
    -

    Integer or character vector specifying the columns to keep +

    keep
    +

    Integer or character vector specifying the columns to keep in the markers matrix, as defined by fcol.

    Value

    - - -

    An updated MSnSet

    - - +

    An updated MSnSet

    See also

    @@ -123,15 +121,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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z%xS4-Qr=E8q}vDs8I;flrk_LXF0$W1Mz6{Q0e23lZiAvH+TgPuaIGx_0Xd0%Y2?D?|@rJig%?H-^S}zMJ92c7U!fJACF37z9GjS zdzqw{o7IUg5?G=V^QM>eX4I&;I5btE_tF8zJLz@i&8e`>zPMSSC8MgpHT7Y~(LWeE zmp*<}cN+=Ql?KY#0;D(Fn4@SR20%vI+x=!hr#8t{jn z_#Ql`n2Ek?-}pRjEbwlbAoZ*G`Nq7y!y-YuA?=Qu}Y&UjM}+!{L<;bk=C;@=gyXN=J%y&iTA0B4H-?FWc*mxI&h+d zk0lSl&5s}y`HeS*syEY|@4jY1HbK$^lqIk^lJWWTjAD;^G}8~5fV*^f5VI^iUEj5x z&fm7rp%>9G%H8pMa%5k|_`x~oH9#9z(2^Vzd1k6@gHUu<>*A!JiC^f62^vq%`*tjf zbRd-+(WLT>Uf9=7E`k;}O$4el?ooBdHknrR1lf-sHWO*WA%ehTo89HUY+4`*nbJUp_m?98|* zL67I22u}1qO>5G4Mmz7WNR+!oITnw0X!iZ8S69}VJyD^#U+?1L&Ksr*d%_O2rajyR zLuUos2=1=gm27b4O(yhfrW~{u`JOqY)jo#z_c^WeS??3&e9o$5u4*~PHJ;(FY&@G< z&WrqmQ{k;iYWXYCDCdvGj;vz*8{dX$%=0fbB1T?f_9a~l0>>o<7B4QQXEVsw^B^gs z8#!G1wI+js6^?eEc&(+?r{(S4^>E#uQ^CYhwS1Ytd2J7Rrqy-KJo)T63eueB9P_&g z!mGGb2;GN5*RJdFvYh)F8T~~{&aisJA7S5YtoEa7nw(9nOk2*q#i#gvO8OU$H -svm classification — svmClassification • pRolocsvm classification — svmClassification • pRoloc - +

    - +
    @@ -88,49 +88,48 @@

    svm classification

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    assessRes
    +
    assessRes

    An instance of class "GenRegRes", as generated by svmOptimisation.

    -
    scores
    +
    scores

    One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

    -
    cost
    +
    cost

    If assessRes is missing, a cost must be provided.

    -
    sigma
    +
    sigma

    If assessRes is missing, a sigma must be provided.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    ...
    +
    ...

    Additional parameters passed to svm from package e1071.

    Value

    - - -

    An instance of class "MSnSet" with

    -

    -

    svm and svm.scores feature variables storing the +

    An instance of class "MSnSet" with + svm and svm.scores feature variables storing the classification results and scores respectively.

    @@ -157,27 +156,27 @@

    Examples

    #> Results #> macro F1: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> 0.9500 0.9509 0.9518 0.9586 0.9629 0.9740 +#> 0.9650 0.9716 0.9783 0.9811 0.9891 1.0000 #> best sigma: 0.1 -#> best cost: 1 +#> best cost: 4 1 plot(params) f1Count(params) -#> 1 -#> 0.1 1 +#> 1 4 +#> 0.1 0 1 levelPlot(params) getParams(params) #> sigma cost -#> 0.1 1.0 +#> 0.1 4.0 res <- svmClassification(dunkley2006, params) #> [1] "markers" getPredictions(res, fcol = "svm") #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 16 178 100 102 135 +#> 20 179 100 103 134 #> Plastid Ribosome TGN vacuole -#> 52 54 17 35 +#> 52 52 16 33 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -197,15 +196,15 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed svm prediction (sigma=0.1 cost=1) Tue Mar 12 07:14:37 2024 -#> Added svm predictions according to global threshold = 0 Tue Mar 12 07:14:37 2024 +#> Performed svm prediction (sigma=0.1 cost=4) Sun Jun 16 09:18:54 2024 +#> Added svm predictions according to global threshold = 0 Sun Jun 16 09:18:54 2024 #> MSnbase version: 1.17.12 getPredictions(res, fcol = "svm", t = 0.75) #> ans #> ER lumen ER membrane Golgi Mitochondrion PM -#> 14 145 71 93 108 +#> 14 144 75 94 109 #> Plastid Ribosome TGN unknown vacuole -#> 42 27 13 149 27 +#> 42 26 13 145 27 #> MSnSet (storageMode: lockedEnvironment) #> assayData: 689 features, 16 samples #> element names: exprs @@ -225,8 +224,8 @@

    Examples

    #> Loaded on Thu Jul 16 22:53:08 2015. #> Normalised to sum of intensities. #> Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015 -#> Performed svm prediction (sigma=0.1 cost=1) Tue Mar 12 07:14:37 2024 -#> Added svm predictions according to global threshold = 0.75 Tue Mar 12 07:14:38 2024 +#> Performed svm prediction (sigma=0.1 cost=4) Sun Jun 16 09:18:54 2024 +#> Added svm predictions according to global threshold = 0.75 Sun Jun 16 09:18:54 2024 #> MSnbase version: 1.17.12 plot2D(res, fcol = "svm") @@ -244,15 +243,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/svmOptimisation.html b/reference/svmOptimisation.html index 5a51c01c..1ec185e6 100644 --- a/reference/svmOptimisation.html +++ b/reference/svmOptimisation.html @@ -1,10 +1,10 @@ -svm parameter optimisation — svmOptimisation • pRolocsvm parameter optimisation — svmOptimisation • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -94,57 +94,57 @@

    svm parameter optimisation

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    cost
    +
    cost

    The hyper-parameter. Default values are 2^-4:4.

    -
    sigma
    +
    sigma

    The hyper-parameter. Default values are 10^(-2:3).

    -
    times
    +
    times

    The number of times internal cross-validation is performed. Default is 100.

    -
    test.size
    +
    test.size

    The size of test data. Default is 0.2 (20 percent).

    -
    xval
    +
    xval

    The n-cross validation. Default is 5.

    -
    fun
    +
    fun

    The function used to summarise the xval macro F1 matrices.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    verbose
    +
    verbose

    A logical defining whether a progress bar is displayed.

    -
    ...
    +
    ...

    Additional parameters passed to svm from package e1071.

    Value

    - - -

    An instance of class "GenRegRes".

    +

    An instance of class "GenRegRes".

    Details

    @@ -177,15 +177,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/tagm-map.html b/reference/tagm-map.html index 2ec7eb44..48118ef4 100644 --- a/reference/tagm-map.html +++ b/reference/tagm-map.html @@ -1,11 +1,11 @@ -The `logPosteriors` function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence. — MAPParams-class • pRolocThe `logPosteriors` function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence. — MAPParams-class • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -79,7 +79,7 @@

    The `logPosteriors` function can be used to extract the log-posteriors at ea

    -
    # S4 method for MAPParams
    +    
    # S4 method for class 'MAPParams'
     show(object)
     
     logPosteriors(x)
    @@ -110,79 +110,81 @@ 

    The `logPosteriors` function can be used to extract the log-posteriors at ea

    Arguments

    -
    object
    + + +
    object

    An MSnbase::MSnSet containing the spatial proteomics data to be passed to tagmMapTrain and tagmPredict.

    -
    x
    +
    x

    An object of class `MAPParams`.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    method
    +
    method

    A charachter() describing the inference method for the TAGM algorithm. Default is "MAP".

    -
    numIter
    +
    numIter

    The number of iterations of the expectation-maximisation algorithm. Default is 100.

    -
    mu0
    +
    mu0

    The prior mean. Default is colMeans of the expression data.

    -
    lambda0
    +
    lambda0

    The prior shrinkage. Default is 0.01.

    -
    nu0
    +
    nu0

    The prior degreed of freedom. Default is ncol(exprs(object)) + 2

    -
    S0
    +
    S0

    The prior inverse-wishary scale matrix. Empirical prior used by default.

    -
    beta0
    +
    beta0

    The prior Dirichlet distribution concentration. Default is 1 for each class.

    -
    u
    +
    u

    The prior shape parameter for Beta(u, v). Default is 2

    -
    v
    +
    v

    The prior shape parameter for Beta(u, v). Default is 10.

    -
    seed
    +
    seed

    The optional random number generator seed.

    -
    params
    +
    params

    An instance of class MAPParams, as generated by tagmMapTrain().

    -
    probJoint
    +
    probJoint

    A logical(1) indicating whether to return the joint probability matrix, i.e. the probability for all classes as a new tagm.map.joint feature variable.

    -
    probOutlier
    +
    probOutlier

    A logical(1) indicating whether to return the probability of being an outlier as a new tagm.map.outlier feature variable. A high value indicates that the protein is @@ -192,11 +194,7 @@

    Arguments

    Value

    - - -

    tagmMapTrain returns an instance of class MAPParams().

    - - +

    tagmMapTrain returns an instance of class MAPParams().

    tagmPredict returns an instance of class MSnbase::MSnSet containing the localisation predictions as a new tagm.map.allocation feature variable.

    @@ -214,7 +212,7 @@

    Details

    Slots

    - +
    method

    A character() storing the TAGM method name.

    @@ -269,15 +267,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/tagm-mcmc.html b/reference/tagm-mcmc.html index e9c1db6e..740e697e 100644 --- a/reference/tagm-mcmc.html +++ b/reference/tagm-mcmc.html @@ -1,11 +1,11 @@ -Localisation of proteins using the TAGM MCMC method — tagmMcmcTrain • pRolocLocalisation of proteins using the TAGM MCMC method — tagmMcmcTrain • pRoloc - +
    @@ -30,7 +30,7 @@
    - +
    @@ -118,75 +118,77 @@

    Localisation of proteins using the TAGM MCMC method

    Arguments

    -
    object
    + + +
    object

    An MSnbase::MSnSet containing the spatial proteomics data to be passed to tagmMcmcTrain and tagmPredict.

    -
    fcol
    +
    fcol

    The feature meta-data containing marker definitions. Default is markers.

    -
    method
    +
    method

    A charachter() describing the inference method for the TAGM algorithm. Default is "MCMC".

    -
    numIter
    +
    numIter

    The number of iterations of the MCMC algorithm. Default is 1000.

    -
    burnin
    +
    burnin

    The number of samples to be discarded from the begining of the chain. Default is 100.

    -
    thin
    +
    thin

    The thinning frequency to be applied to the MCMC chain. Default is 5.

    -
    mu0
    +
    mu0

    The prior mean. Default is colMeans of the expression data.

    -
    lambda0
    +
    lambda0

    The prior shrinkage. Default is 0.01.

    -
    nu0
    +
    nu0

    The prior degreed of freedom. Default is ncol(exprs(object)) + 2

    -
    S0
    +
    S0

    The prior inverse-wishart scale matrix. Empirical prior used by default.

    -
    beta0
    +
    beta0

    The prior Dirichlet distribution concentration. Default is 1 for each class.

    -
    u
    +
    u

    The prior shape parameter for Beta(u, v). Default is 2

    -
    v
    +
    v

    The prior shape parameter for Beta(u, v). Default is 10.

    -
    numChains
    +
    numChains

    The number of parrallel chains to be run. Default it 4.

    -
    BPPARAM
    +
    BPPARAM

    Support for parallel processing using the BiocParallel infrastructure. When missing (default), the default registered BiocParallelParam parameters are @@ -196,18 +198,18 @@

    Arguments

    details.

    -
    params
    +
    params

    An instance of class MCMCParams, as generated by tagmMcmcTrain().

    -
    probJoint
    +
    probJoint

    A logical(1) indicating whether to return the joint probability matrix, i.e. the probability for all classes as a new tagm.mcmc.joint feature variable.

    -
    probOutlier
    +
    probOutlier

    A logical(1) indicating whether to return the probability of being an outlier as a new tagm.mcmc.outlier feature variable. A high value indicates that the protein is @@ -217,31 +219,21 @@

    Arguments

    Value

    - - -

    tagmMcmcTrain returns an instance of class +

    tagmMcmcTrain returns an instance of class MCMCParams.

    - -

    tagmMcmcPredict returns an instance of class MSnbase::MSnSet containing the localisation predictions as a new tagm.mcmc.allocation feature variable. The allocation -probability is encoded as tagm.mcmc.probability

    - - -

    (corresponding to the mean of the distribution +probability is encoded as tagm.mcmc.probability +(corresponding to the mean of the distribution probability). In additionm the upper and lower quantiles of the allocation probability distribution are available as tagm.mcmc.probability.lowerquantile and tagm.mcmc.probability.upperquantile feature variables. The -Shannon entropy is available in the tagm.mcmc.mean.shannon

    - - -

    feature variable, measuring the uncertainty in the allocations +Shannon entropy is available in the tagm.mcmc.mean.shannon +feature variable, measuring the uncertainty in the allocations (a high value representing high uncertainty; the highest value is the natural logarithm of the number of classes).

    - -

    tagmMcmcProcess returns an instance of class MCMCParams with its summary slot populated.

    @@ -283,15 +275,15 @@

    See also

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/testMSnSet.html b/reference/testMSnSet.html index 79aa4e13..93c60d0e 100644 --- a/reference/testMSnSet.html +++ b/reference/testMSnSet.html @@ -1,13 +1,13 @@ -Create a stratified 'test' MSnSet — testMSnSet • pRolocCreate a stratified 'test' MSnSet — testMSnSet • pRoloc - +
    @@ -32,7 +32,7 @@
    - +
    -

    This function creates a stratified 'test' MSnSet which can be used +

    This function creates a stratified 'test' MSnSet which can be used for algorihtmic development. A "MSnSet" containing only -the marker proteins, as defined in fcol, is returned with a new +the marker proteins, as defined in fcol, is returned with a new feature data column appended called test in which a stratified subset of these markers has been relabelled as 'unknowns'.

    @@ -88,30 +88,30 @@

    Create a stratified 'test' MSnSet

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet"

    -
    fcol
    +
    fcol

    The feature meta-data column name containing the marker definitions on which the data will be stratified. Default is markers.

    -
    size
    +
    size

    The size of the data set to be extracted. Default is 0.2 (20 percent).

    -
    seed
    +
    seed

    The optional random number generator seed.

    Value

    - - -

    An instance of class "MSnSet" which +

    An instance of class "MSnSet" which contains only the proteins that have a labelled localisation i.e. the marker proteins, as defined in fcol and a new column in the feature data slot called test which has part @@ -157,15 +157,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/testMarkers.html b/reference/testMarkers.html index 75a62418..74512d8e 100644 --- a/reference/testMarkers.html +++ b/reference/testMarkers.html @@ -1,12 +1,12 @@ -Tests marker class sizes — testMarkers • pRolocTests marker class sizes — testMarkers • pRoloc - +
    @@ -31,7 +31,7 @@
    - +
    @@ -86,35 +86,35 @@

    Tests marker class sizes

    Arguments

    -
    object
    + + +
    object

    An instance of class "MSnSet".

    -
    xval
    +
    xval

    The number cross-validation partitions. See the xval argument in the parameter optimisation function(s). Default is 5.

    -
    n
    +
    n

    Number of additional examples.

    -
    fcol
    +
    fcol

    The name of the prediction column in the featureData slot. Default is "markers".

    -
    error
    +
    error

    A logical specifying if an error should be thown, instead of a warning.

    Value

    - - -

    If successfull, the test invisibly returns NULL. Else, +

    If successfull, the test invisibly returns NULL. Else, it invisibly returns the names of the classes that have too few examples.

    @@ -166,15 +166,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/thetas.html b/reference/thetas.html index b08fb849..57dbd465 100644 --- a/reference/thetas.html +++ b/reference/thetas.html @@ -1,5 +1,5 @@ -Draw matrix of thetas to test — thetas • pRolocDraw matrix of thetas to test — thetas • pRoloc - +
    @@ -33,7 +33,7 @@
    - +
    @@ -90,29 +90,29 @@

    Draw matrix of thetas to test

    Arguments

    -
    nclass
    + + +
    nclass

    Number of marker classes

    -
    by
    +
    by

    The increment of the weights. One of 1, 0.5, 0.25, 2, 0.1 or 0.05.

    -
    length.out
    +
    length.out

    The desired length of the weight sequence.

    -
    verbose
    +
    verbose

    A logical indicating if the weight sequences should be printed out. Default is TRUE.

    Value

    - - -

    A matrix with all possible theta weight combinations.

    +

    A matrix with all possible theta weight combinations.

    Author

    @@ -155,15 +155,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/reference/undocumented.html b/reference/undocumented.html index 22912321..cb56d289 100644 --- a/reference/undocumented.html +++ b/reference/undocumented.html @@ -1,10 +1,10 @@ -Undocumented/unexported entries — undocumented • pRolocUndocumented/unexported entries — undocumented • pRoloc - +
    @@ -29,7 +29,7 @@
    - +
    @@ -94,15 +94,15 @@

    Author

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

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    @@ -35,7 +35,7 @@
    - +
    @@ -94,31 +94,31 @@

    Compute the number of non-zero values in each marker classes

    Arguments

    -
    object
    + + +
    object

    An instance of class MSnSet with binary data.

    -
    fcol
    +
    fcol

    A character defining the feature data variable to be used as markers. Default is "markers".

    -
    as.matrix
    +
    as.matrix

    If TRUE (default) the data is formatted and returned as a matrix. Otherwise, a list is returned.

    -
    percent
    +
    percent

    If TRUE, percentages are returned. Otherwise, absolute values.

    Value

    - - -

    A matrix or a list indicating the number of +

    A matrix or a list indicating the number of non-zero value per marker class.

    @@ -349,15 +349,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.9000.

    +

    Site built with pkgdown 2.0.9.9000.

    - - + + diff --git a/sitemap.xml b/sitemap.xml index c0a41461..2f1b7620 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1,282 +1,96 @@ - - - - /404.html - - - /CONDUCT.html - - - /articles/index.html - - - /articles/v01-pRoloc-tutorial.html - - - /articles/v02-pRoloc-ml.html - - - /articles/v03-pRoloc-bayesian.html - - - /articles/v04-pRoloc-goannotations.html - - - /articles/v05-pRoloc-transfer-learning.html - - - /authors.html - - - /index.html - - - /news/index.html - - - /reference/AnnotationParams-class.html - - - /reference/ClustDist-class.html - - - /reference/ClustDistList-class.html - - - /reference/GenRegRes-class.html - - - /reference/MCMCParams.html - - - /reference/MLearn-methods.html - - - /reference/MartInstance-class.html - - - /reference/QSep-class.html - - - /reference/SpatProtVis-class.html - - - /reference/addGoAnnotations.html - - - /reference/addLegend.html - - - /reference/addMarkers.html - - - /reference/checkFeatureNamesOverlap.html - - - /reference/checkFvarOverlap.html - - - /reference/chi2-methods.html - - - /reference/classWeights.html - - - /reference/clustDist.html - - - /reference/defunct.html - - - /reference/empPvalues.html - - - /reference/fDataToUnknown.html - - - /reference/filterBinMSnSet.html - - - /reference/filterMaxMarkers.html - - - /reference/filterMinMarkers.html - - - /reference/filterZeroCols.html - - - /reference/getGOFromFeatures.html - - - /reference/getMarkerClasses.html - - - /reference/getMarkers.html - - - /reference/getNormDist.html - - - /reference/getPredictions.html - - - /reference/getStockcol.html - - - /reference/goIdToTerm.html - - - /reference/highlightOnPlot.html - - - /reference/index.html - - - /reference/knnClassification.html - - - /reference/knnOptimisation.html - - - /reference/knntlClassification.html - - - /reference/knntlOptimisation.html - - - /reference/ksvmClassification.html - - - /reference/ksvmOptimisation.html - - - /reference/makeGoSet.html - - - /reference/markerMSnSet.html - - - /reference/markers.html - - - /reference/mcmc-helpers.html - - - /reference/minMarkers.html - - - /reference/mixing_posterior_check.html - - - /reference/move2Ds.html - - - /reference/mrkConsProfiles.html - - - /reference/mrkHClust.html - - - /reference/nbClassification.html - - - /reference/nbOptimisation.html - - - /reference/nicheMeans2D.html - - - /reference/nndist-methods.html - - - /reference/nnetClassification.html - - - /reference/nnetOptimisation.html - - - /reference/orderGoAnnotations.html - - - /reference/orgQuants.html - - - /reference/pRolocmarkers.html - - - /reference/perTurboClassification.html - - - /reference/perTurboOptimisation.html - - - /reference/phenoDisco.html - - - /reference/plot2D.html - - - /reference/plot2Ds.html - - - /reference/plotConsProfiles.html - - - /reference/plotDist.html - - - /reference/plotEllipse.html - - - /reference/plsdaClassification.html - - - /reference/plsdaOptimisation.html - - - /reference/rfClassification.html - - - /reference/rfOptimisation.html - - - /reference/sampleMSnSet.html - - - /reference/showGOEvidenceCodes.html - - - /reference/spatial2D.html - - - /reference/subsetMarkers.html - - - /reference/svmClassification.html - - - /reference/svmOptimisation.html - - - /reference/tagm-map.html - - - /reference/tagm-mcmc.html - - - /reference/testMSnSet.html - - - /reference/testMarkers.html - - - /reference/thetas.html - - - /reference/undocumented.html - - - /reference/zerosInBinMSnSet.html - + +/404.html +/CONDUCT.html +/articles/index.html +/articles/v01-pRoloc-tutorial.html +/articles/v02-pRoloc-ml.html +/articles/v03-pRoloc-bayesian.html +/articles/v04-pRoloc-goannotations.html +/articles/v05-pRoloc-transfer-learning.html +/authors.html +/index.html +/news/index.html +/reference/AnnotationParams-class.html +/reference/ClustDist-class.html +/reference/ClustDistList-class.html +/reference/GenRegRes-class.html +/reference/MCMCParams.html +/reference/MLearn-methods.html +/reference/MartInstance-class.html +/reference/QSep-class.html +/reference/SpatProtVis-class.html +/reference/addGoAnnotations.html +/reference/addLegend.html +/reference/addMarkers.html +/reference/checkFeatureNamesOverlap.html +/reference/checkFvarOverlap.html +/reference/chi2-methods.html +/reference/classWeights.html +/reference/clustDist.html +/reference/defunct.html +/reference/empPvalues.html +/reference/fDataToUnknown.html +/reference/filterBinMSnSet.html +/reference/filterMaxMarkers.html +/reference/filterMinMarkers.html +/reference/filterZeroCols.html +/reference/getGOFromFeatures.html +/reference/getMarkerClasses.html +/reference/getMarkers.html +/reference/getNormDist.html +/reference/getPredictions.html +/reference/getStockcol.html +/reference/goIdToTerm.html +/reference/highlightOnPlot.html +/reference/index.html +/reference/knnClassification.html +/reference/knnOptimisation.html +/reference/knntlClassification.html +/reference/knntlOptimisation.html +/reference/ksvmClassification.html +/reference/ksvmOptimisation.html +/reference/makeGoSet.html +/reference/markerMSnSet.html +/reference/markers.html +/reference/mcmc-helpers.html +/reference/minMarkers.html +/reference/mixing_posterior_check.html +/reference/move2Ds.html +/reference/mrkConsProfiles.html +/reference/mrkHClust.html +/reference/nbClassification.html +/reference/nbOptimisation.html +/reference/nicheMeans2D.html +/reference/nndist-methods.html +/reference/nnetClassification.html +/reference/nnetOptimisation.html +/reference/orderGoAnnotations.html +/reference/orgQuants.html +/reference/pRolocmarkers.html +/reference/perTurboClassification.html +/reference/perTurboOptimisation.html +/reference/phenoDisco.html +/reference/plot2D.html +/reference/plot2Ds.html +/reference/plotConsProfiles.html +/reference/plotDist.html +/reference/plotEllipse.html +/reference/plsdaClassification.html +/reference/plsdaOptimisation.html +/reference/rfClassification.html +/reference/rfOptimisation.html +/reference/sampleMSnSet.html +/reference/showGOEvidenceCodes.html +/reference/spatial2D.html +/reference/subsetMarkers.html +/reference/svmClassification.html +/reference/svmOptimisation.html +/reference/tagm-map.html +/reference/tagm-mcmc.html +/reference/testMSnSet.html +/reference/testMarkers.html +/reference/thetas.html +/reference/undocumented.html +/reference/zerosInBinMSnSet.html +
    @@ -29,7 +29,7 @@