Update README.md

README.md

@@ -8,23 +8,29 @@ The purpose of this app is to develop an interactive Shiny portal that can dehas
 </p>
 
 ## Table of Contents
-
-- [Template](#team-repo-template)
-    - [Background](#Background)
-    - [Example Data](#Example Data)
-    - [Usage](#usage)
-        - [Installation](#installation)
-        - [Dependecies](#requirements)
-    - [Results](#results) _Optional depending on project_
-    - [Team Members](#team-members)
+ - [Background](#background)
+ - [Example Data](#exampledata)
+ - [Usage](#usage)
+     - [Installation](#installation)
+     - [Dependecies](#requirements)
+ - [Plan](#plan)
+     - [Module-1](#module-1)
+     - [Module-2](#module-2)
+     - [Module-3](#module-3)
+     - [Module-4](#module-4)
+     - [Module-5](#module-5)
+     - [Module-6](#module-6)
+     - [Module-7](#module-7)
+     - [Module-8](#module-8)
+ - [Results](#results)
+ - [Team Members](#team-members)
 
 ## Background
 
-Analysis of cost effective methods spawned techniques like CITE-Seq that pool samples from different experimental conditions/replicates/library preps which are sequenced to get a high throughput. There are tools like Seurat (Sajita Lab - https://satijalab.org/seurat/articles/hashing_vignette.html) who already developed a method (HTODemux) that can demultiplex pooled samples. So, through this Shiny app, we will be making it easy for anyone in the field of omics to QC, Demultiplex, Visualize, normalize and perform some downstream analysis.
-
+Analysis of cost effective methods spawned techniques like CITE-Seq that pool samples from different experimental conditions/replicates/library preps which are sequenced to get a high throughput. There are tools like Seurat (Sajita Lab - https://satijalab.org/seurat/articles/hashing_vignette.html); who already developed a method (HTODemux) that can demultiplex pooled samples. So, through this Shiny app, we will be making it easy for anyone in the field of omics to QC, Demultiplex, Visualize, normalize and perform downstream analysis for multiplex single-cell data.
 
 
-## Example Data
+## ExampleData
 
 Summary of the Study:
 
@@ -50,23 +56,79 @@ Once you start the app, it should open up a Rshiny pop-up window (allow pop-up o
 
 Works on Mac, windows and Linux (gui)
 
-*R Libaries required:*
-
-Seurat
-dplyr
-plotly
-ggplot2
-clusterprofiler
-knitr
-kableExtra
-cowplot
-gridExtra
-tidyverse
-biomaRt
-Matrix
-stringr
-DoubletFinder (optional)
-SingleR (optional - Cell annotation Tool)
+*R Libraries required:*
+
+Seurat  
+dplyr  
+plotly  
+ggplot2  
+clusterprofiler  
+knitr  
+kableExtra  
+cowplot  
+gridExtra  
+tidyverse  
+biomaRt  
+Matrix  
+stringr  
+DoubletFinder (optional)  
+SingleR (optional - Cell annotation Tool)  
+
+## Plan
+
+Incudes Following Modules:
+
+### Module-1
+
+Quality Control - Visualization  
+
+<p align="center">
+
+ ![Example_QC_1](https://user-images.githubusercontent.com/22992035/182963897-e8983b6c-b3bb-4d63-b1e6-72d8815e73bf.png)
+
+</p>
+
+### Module-2
+
+Demultiplexing and Doublet detection module – Visualization (optional)  
+
+<p align="center">
+
+ ![Example_QCmetrics](https://user-images.githubusercontent.com/22992035/182962598-05e561c1-576f-4c46-8b49-225029d37d9c.png)  
+
+</p>
+
+
+### Module-3 
+
+Identifying Cell subset module by unsupervised learning – Visualization (UMAPS, TSNE, PCA, Elbow plot).  
+
+<p align="center">
+
+![Example_Umap_DehashedSamples](https://user-images.githubusercontent.com/22992035/182963974-89006d82-b05b-47ae-a070-a2141c8199e4.png)
+
+</p>
+
+### Module-4 
+
+Integration – no visualization (background process – time taking step, dependent on no. of Capture or samples per capture or no. of cells in the experiment).  
+
+### Module-5 
+
+Differential expression module – Visualization (Heatmaps, RidgePlots, FeaturePlots, DotPlots).  
+
+### Module-6
+
+Gene set enrichment analysis module – Visualization (Upset plots, enrichment plots).  
+
+### Module-7
+
+Multi-gene set comparison module – Comparison of genes in Hallmark vs KEGG pathways.  
+
+### Module-8
+
+Visualization module – Additional plots like Marker genes expression across multiple treatment groups.  
+
 
 ## Results
 
@@ -75,9 +137,9 @@ Need to be posted yet.
 ## Team Members
 
 Arun Boddapati | [email protected] | Team Leader  
-Srinivas Nallandighal |  [email protected] | Team Member
-Ojonugwa Abubakar |  [email protected] | Team Member
-JaMor Hairston | [email protected] | Team Member
-Jason Needham | [email protected] | Team Member
-Aaron Lynch | [email protected] | Team Member
-Antony Linto | [email protected] | Team Member
+Srinivas Nallandighal | [email protected] | Team Member  
+Ojonugwa Abubakar | [email protected] | Team Member  
+JaMor Hairston | [email protected] | Team Member  
+Jason Needham | [email protected] | Team Member  
+Aaron Lynch | [email protected] | Team Member  
+Antony Linto | [email protected] | Team Member