RNA-seq Analysis Tool

This applications provide an interactive interface for analyzing RNA sequencing data.

Table of Contents

• Installation
• Running the Application
• Requirements
• Required R Packages

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Installation

1. Clone this repository to your local machine using Git:

   git clone https://github.com/lmilazzo/RNA-seq-Analysis-Tool.git
   cd RNA-seq-Analysis-Tool

   Alternatively, if you do not have Git installed, you can download the repository as a ZIP file:

   • Click the green "Code" button at the top of the repository page on GitHub.
   • Select "Download ZIP."
   • Extract the downloaded ZIP file to a directory of your choice.

2. Ensure you have R installed on your system. You can download it from CRAN.

3. Install the required R packages listed in the Required R Packages section.

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Running the Application

To run the application, use the provided run_app.R script contained in either the Version 1.0.0 or Version 2.0.0 directories. Version 1.0.0 is not recomended and some pages may break.

1. Open your R console or an R IDE (e.g., RStudio).

2. Set your working directory to the location of the desired version run_app.R script. For example:

   setwd("path/to/RNA-seq-Analysis-Tool/Version 2.0.0")

3. Run the script to launch the application

   If in Bash Terminal/Console

   Rscript run_app.R

   If in RStudio click 'run app' in the top right of your source file pane.

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Requirements

• A working installation of R (version 4.0 or higher is recommended).
• Internet connection if you think you may need to install some of the packages.

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Required R Packages

Below is a list of R packages required to run the application. Using the run_app.R script to launch the application should download any needed packages however if you are having trouble it is recommended to do it manually.

  library(shiny) #Used to make the interactive app
  library(shinythemes) #Used to add ui themes
  library(shinyjs) #Used for javascript additions
  library(shinyFiles) #Saving files
  library(DT) #interactive data tables
  library(htmlwidgets) #additional html items and features
  library(devtools) #Used for installing github packages
  
  # For data
  library(dplyr) #Data wrangling 
  library(tidyr) #Data wrangling 
  library(readr) #reading and writing files
  library(DESeq2) #Used for Differential Gene Expression
  library(SummarizedExperiment) #Used for Differential Gene Expression
  devtools::install_github("Lmilazzo/BioVis")
  library(BioVis) #My R package from github with plotting functions (Only used in Version 1.0.0)
  library(pathfindR) #Used for Pathway Enrichment 
  
  # For plotting
  library(ggplot2) #Visualizations
  library(ggplotify)#Visualizations
  library(pheatmap)#Visualizations
  library(ggbeeswarm)#Visualizations
  library(ggrepel)#Visualizations
  library(plotly)#Visualizations
  library(grid)#Visualizations
  
  # Additional
  library(zip) #Downloading your saved session files to your local file system.

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File Usage Documentation

Basic File Rules

• Headers are case sensitive.
• All files should be CSV or TSV format.
• All listed required columns must be present for the file to be accepted.
• Any ID-type columns must not have duplicate values.
• All sample columns must include a header that starts with a . character to be identified by the application.

File Type Summaries and Examples

1. Metadata File

Maps each sample to a row of variables that describe the sample.

Required Columns:

• sample: Contains the sample names, which should match the sample names in other uploaded files. (The application automatically prepends a . to sample names if missing.)

Example:

sample	condition
.sample1	treatment
.sample2	control
.sample3	treatment

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2. Raw Count Table

A matrix of counts representing the number of times each gene occurred within each sample.

Required Columns:

• gene_id: Unique identifier for each gene.
• gene_name: Name of the gene (recommended to represent actual genes).
• Sample columns: Contain raw count data.

Example:

gene_id	gene_name	.sample1	.sample2	.sample3
G1	NME1	100	200	150
G2	AK1	50	75	60
G3	AK6	400	410	420

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3. Differential Expression Analysis Results Output

Not all the columns that are returned from DESeq2 are required to use this file. DESeq2 does not append the sample counts to the results, rather this must be done manually before uploading with the DESeq results and the normalized counts matrix given by DESeq2.

Required Columns:

• gene_id: Unique identifier for each gene.
• gene_name: Name of the gene.
• log2FoldChange: Log2 fold change value.
• padj: Adjusted p-value.

Example:

gene_id	gene_name	log2FoldChange	padj	.sample1	.sample2
G13	DCK	-1.2	0.05	120	100
G65	ADSS2	2.3	0.01	210	230
G4	ADSL	1.8	0.003	400	450

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4. Abundance Data File

A matrix of normalized counts for each gene and sample to be used in the pathway analysis portion of the application. The normalized counts can be retrieved from a DESeq2 experiment and modified to the required format.

Required Columns:

• gene_name: Name of the gene (must represent actual genes).
• Sample columns: Contain normalized count data.

Example:

gene_name	.sample1	.sample2	.sample3
ENPP3	1.2	1.1	1.3
APRT	1.6	1.5	1.4
RRM2	2.1	2.2	2.0

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5. Previous Pathway Analysis Results Output

The output/results from a previous pathfindR pathway analysis experiment. The results must include a clustered column, as pathfindR clusters pathways automatically.

Required Columns:

• ID: Unique identifier for each pathway.
• Term_Description: Name of the pathway.
• Fold_Enrichment: Fold enrichment value.
• occurrence: Integer representing the number of occurrences of the pathway.
• support: Float value representing the pathway's support.
• lowest_p: Lowest p-value in the pathway.
• highest_p: Highest p-value in the pathway.
• non_Signif_Snw_Genes: Comma-separated list of non-significant genes in the pathway.
• Up_regulated: Comma-separated list of upregulated genes.
• Down_regulated: Comma-separated list of downregulated genes.
• all_pathway_genes: Comma-separated list of all genes in the pathway.
• num_genes_in_path: Number of genes in the pathway.
• Cluster: Cluster ID (integer).
• Status: Either Representative or Member.

Example:

ID	Term_Description	Fold_Enrichment	occurrence	support	lowest_p	highest_p	Cluster	Status	non_Signif_Snw_Genes	Up_regulated	Down_regulated	all_pathway_genes	num_genes_in_path
P01	Prion Disease	2.5	5	0.8	0.0001	0.1	1	Representative	GMPS, CMPK1	TK1, DUT	TYMS, CTPS1	GMPS, CMPK1, TK1, DUT, TYMS, CTPS1	6
P02	Huntington Disease	1.8	3	0.5	0.00003	0.02	2	Member	UCK2, NT5C3B	HPRT1, IMPDH2	UCK2, NT5C3B, HPRT1, IMPDH2	4

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When to Use Each File

The application can be experienced in its entirety with the use of just the two following files: Metadata and Raw Counts Table.

1. Metadata files are required with any use of the application.
2. Raw Count Table is used when a new Differential Gene Expression Analysis needs to be done. The application can then directly run the results into the pathway analysis portion of the application.

The next three files are useful if an experiment has already been done outside of the application with DESeq2 or pathfindR and the user just wants access to the visuals the application provides. This allows more flexibility if the user wishes to do advanced experimental designs, formulas, or custom filtering of the results, which are not accessible within the application. The downside, however, is that some features may become unavailable due to missing information.

3. Differential Expression Analysis Results Output is used in conjunction with a Metadata file to skip the process of running DESeq2 and to just visualize results that have already been acquired. After uploading these two files, you may also directly pass them into the pathway analysis portion of the application without uploading anything else.
4. Abundance Data and Pathway Analysis Results Output may be used in conjunction with Metadata to bypass the rest of the application and just visualize and explore previously acquired results from a pathway analysis. An abundance data file is not required to use this method; however, some features may not be available without it.

Saving Files Documentation

All main plots shown in the application can be saved as a PDF file.

How to Save a File

• Click the green Save Plot or Save Results button found on the page this will save the currently displayed plot on that page, or in the case of save Results will save the unfiltered output or Diffrential Gene Expression or Pathway Enrichment Analysis displayed in the corresponding table.
• You will be prompted to enter a name for the file.
• After inputting the name and closing the pop-up, the file will be stored in a background directory.
• This directory is cleared upon closing the application & you will lose any files that you saved unless you complete the following download step

Downloading Saved Files

• Locate the “Download Saved Files” button at the top right of any page.
• Clicking this button will download a zipped directory containing all saved plots or results from your session. A default name of "SessionDownloadDate Time" will be provided for the directory.