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Modular Bulk RNA-seq Analysis RShiny Tool Suite

License: MIT R Version

Author: Eren Ada, PhD
Date: 12/01/2025
Current Version: 2.0.0

Project Overview

This project develops a comprehensive and modular bulk RNA-sequencing (RNA-seq) analysis tool using RShiny. The suite consists of interconnected RShiny applications, each dedicated to a specific stage of the RNA-seq analysis workflow.

Current Status: QC & Pre-processing Tool

Production-ready QC and pre-processing for bulk RNA‑seq count data. See Screenshots below for a brief tour.

Module 1: QC & Pre-processing Tool

Status: Complete and Production-Ready
Location: app.R (main application)

Features

  • Data Input & Validation
  • Quality Control: library size, expression distribution, correlation heatmaps, 2D/3D PCA
  • Filtering & Normalization: multiple strategies and evaluation views
  • Export: processed data and publication-ready plots

Quick Start

# Install dependencies (once)
Rscript scripts/install_dependencies.R

# Launch from repository root
shiny::runApp("app.R")

For detailed usage instructions, see the Quick Start Guide.

Documentation

For Users

For Developers

System Requirements

  • R Version: 4.0.0 or higher (4.3.0+ recommended)
  • Operating System: Windows 10+, macOS 10.14+, or Linux (Ubuntu 18.04+)
  • RAM: 4GB minimum (8GB recommended)
  • Browser: Chrome, Firefox, Safari, or Edge (latest versions)

Installation & Setup

Install Dependencies (Recommended)

Install all required CRAN and Bioconductor packages:

Rscript scripts/install_dependencies.R

# Then launch the application
shiny::runApp("app.R")

Manual Setup

For development or troubleshooting:

# Install package manager
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Install core dependencies
BiocManager::install(c("DESeq2", "edgeR"))
install.packages(c("shiny", "DT", "plotly", "ggplot2"))

# Note: The app uses additional packages (ggrepel, corrplot, shinythemes, shinyWidgets, 
# dplyr, tidyr, readr, viridis, RColorBrewer, scales, moments, e1071, matrixStats, 
# and Bioconductor preprocessCore, SummarizedExperiment and friends). 
# To avoid omissions, prefer running: Rscript scripts/install_dependencies.R

Screenshots & Interface Overview

Key views:

  • Data Input & Validation
  • QC: PCA (2D/3D), correlation heatmaps
  • Filtering & Normalization with evaluation

Screenshots:

  • Validation: screenshots/validation.png
  • PCA: screenshots/pca.png
  • Correlation: screenshots/sample_correlation.png
  • Filtering/Normalization: screenshots/filtering_normalization.png
  • About: screenshots/about.png

Example Usage

Basic Workflow

  1. Data Upload: Load count matrix and metadata (CSV format)
  2. Validation: Review data validation results and handle any issues
  3. Quality Control: Examine QC plots and assess data quality
  4. Processing: Apply filtering and normalization based on QC results
  5. Export: Download processed data and reports

Example Data

Sample datasets are provided in the example_data/ directory for testing and learning:

  • example_counts.csv - Anonymized count matrix (5,001 genes × 24 samples)
  • example_metadata.csv - Corresponding sample metadata with experimental design

Contributing

Contributions are welcome. See the Developer Documentation for setup, standards, testing, and PR guidance.

Quick Contribution Steps

  1. Fork the repository
  2. Create a feature branch
  3. Follow coding standards in Developer Guide
  4. Add tests for new functionality
  5. Update documentation
  6. Submit pull request

Support & Community

Getting Help

  1. Check the Troubleshooting Guide
  2. Review Technical Requirements
  3. Try with example data to isolate issues
  4. Create an issue with detailed information

License

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

Disclaimer

This software is provided for research purposes only and has not been reviewed or approved by the Food and Drug Administration or any other regulatory agency. Clinical applications are neither recommended nor advised. Any use of this software is at the sole risk of the user.

For questions about usage or licensing, please contact: erenada@gmail.com

Citation

If you use this tool in your research, please cite:

@software{ada2025rna,
  author = {Ada, Eren},
  title = {Modular Bulk RNA-seq Analysis RShiny Tool Suite},
  year = {2025},
  version = {2.0.0},
  url = {https://github.com/hms-immunology/RNA_QC_APP}
}

Acknowledgments

This project builds upon the excellent work of the Bioconductor community, particularly the DESeq2, edgeR, and limma packages for RNA-seq analysis.