This is a program for the analysis of ATAC-seq or ChIP-seq data in R. This package incorporates the following core packages for data analysis and visualization:
DiffBind
ChIPseeker
Clusterprofiler
Ggplot2
Pheatmap
The app.R can be downloaded and opened in R-studios. Please download the Required_Packages.R file and run it, which will attempt to automatically install the required packages.
Ensure you have R version 4.1.2 or higher for compatibility.
- Open Terminal and maneuver to the directory containing the
Required_Packages.Randapp.Rfiles. Type the following command:
R < Required_Packages.R --no-save
- Wait for the installation of required packages to complete. Then type the following command in the terminal:
Rscript -e 'library(methods); shiny::runApp("app.R", launch.browser = TRUE)'
- This should launch the shiny dashboard app in the browser.
A docker container will be available soon for easy installation and running of the software
The sample file contains metadata on the following main items:
- Sample name and identification
- Condition (i.e. control sample and Sample with a treatment)
- Location of the binary assignment file (BAM)
- Location of the tab-delimted text file containing feature tracks
A pre-filled sample file is included in the GitHub page for reference. Additionally, an empty template is also included in the page
The following series of steps will outline the ways that CAPPS can be used for ChIP-seq or ATAC-seq data analysis.
Before using CAPPS, please ensure that you run the
Required_Packages.Rfile which will attempt to install packages that may be missing.
Click on the browse button to load the sample sheet. The sample sheet, once loaded, will be displayed beside the control panel. The arrows at the bottom of the display box/panel allows easy maneuvering of the various tabs in the applicaiton.
The Help tab contains extra information on preparation of the sample sheet.
Please Note You can proceed directly to differential gene expression analysis if your reference genome is mouse. Otherwise go to Tab2 or press the right arrow below the panel to proceed to next page.
This page allows the user to set main settings for the experiment. These entail selection of the reference genome, as well as control and treatment conditions.
The options in Advanced Settings tab are for manipulating settings for differential peak analysis. For further information on the advanced settings, please check out the DiffBind resource:
Maneuvering to this page initiates the differential peak analysis, peak annotation, and filtering. The output contains information on: - The number of differential enriched regions - The number of peaks with positive and negative fold enrichment relative to the control sample - The number of peaks that meet p-value and FDR cutoffs. - Genomic annotation of the peaks - Annotation of the nearest gene
The control panel also contains options to download the full table, or a table that has been pre-filtered for positive or negative fold enrichment relative to the control samples. You can also download a table that has been adjusted to FDR < 0.2.
This page contains some of the exploratory plots for sample correlation. These include the PCA, MA, heatmap and volcano plots.
This page contains annotation for the differential enriched regions in the genome. The annotation was performed via the ChIPseeker package.
The peaks can be filtered by FDR and regions with positive or negative fold change enrichment relative to the control sample.
The help page provides information on the interpretation of the data.
The pathways analysis can be performed using the KEGG or REACTOME databases. The pathways analysis for positive versus negative fold change regions are divided into separate tabs. Depending upon the database used, the plots generated will vary. After pathways analysis, the table with full pathway analysis and genes lists can be downloaded.
The number of pathways that are displayed in the plots can be varied. Additionally, there are options to view the data in box, CNET, bar or heat plots.
This page is to view the heatmap performed on the differentially enriched regions. There are various options available for the heatmap plot, such as the algorithyms for hierarchial clustering, color schemes and clustering by rows and/or columns.
This page is designed to view bar graphs on gene(s) specific plots. This will provide the end user an idea if peaks near the gene(s) of interest are enriched or depleted relative to the control samples. The datatable that is filtered for the input gene list can be downloaded.
