/GeneDifferentialExplorer

A Shiny App for automating the analysis pipeline for RNASeq data using EdgeR package.

Primary LanguageRMIT LicenseMIT

Gene Differential Expression Explorer

This R Shiny application is designed for exploring gene differential expression in RNASeq data using various statistical techniques powered by EdgeR package and visualizations.

Author

  • HanChen Wang

App Demo

Below is a quick demo of the app in action (you can also view it as a video in Images):

Demo GIF

Features

  • File Uploads and Data Handling:
    • Upload metadata files (.csv, .txt) containing grouping information.
    • Upload featureCount files for differential expression analysis.
    • Practice with demo data option available.
  • Exploratory Data Analysis:
    • Visualize raw gene expression through boxplots, relative level of expression (RLE), and PCA plots.
    • Filter and summarize gene counts based on user-defined criteria.
  • Normalization Techniques:
    • Apply Quantile normalization using preprocessCore.
    • Perform TMM normalization with statistical summaries and visualizations.
  • Data Visualization:
    • Visualize percentage of total variance covered by each Principal Component (PC).
    • Plot interactive PCA graphs based on user-selected components.
  • EdgeR Pairwise Analysis:
    • Conduct pairwise differential expression analysis using EdgeR.
    • Generate comparison tables and volcano plots with customizable significance thresholds.

Usage

  1. Setup and Installation:
    • Ensure R and required libraries (shiny, edgeR, tidyverse, etc.) are installed.
    • Clone this repository to your local machine.
  2. Run the App:
    • Open R or RStudio.
    • Set the working directory to the app directory.
    • Run shiny::runApp() in the R console.
  3. Usage Instructions:
    • Navigate through different tabs for specific functionalities.
    • Upload files and explore data using the sidebar and main panels.
    • Follow on-screen instructions for each analysis step.

Additional Notes

  • Adjust themes and visual styles using bslib for customized UI experience.
  • Modify analysis parameters and thresholds directly in the app interface.
  • For detailed technical documentation, refer to the code comments and the respective R packages' documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.