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):

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.