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DEG Pipeline & Visualizer v2.0.4

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@alirezabk1382927-sys alirezabk1382927-sys released this 26 Jul 19:29
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v2.0.4
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DEG Pipeline & Visualizer

An integrated desktop application for Differential Gene Expression (DEG) analysis and publication-quality visualization

📖 About the Project

DEG Pipeline & Visualizer is a graphical (GUI) tool that simplifies the RNA-seq analysis workflow, from raw data to final plots. It uses PyDESeq2 to perform differential gene expression analysis between two groups (e.g., Tumor vs. Normal) and provides a variety of high-quality visualization options.

No Python installation or extra dependencies are required — just download the executable for your operating system and run it.

✨ Key Features

  • Automated DEG analysis using PyDESeq2
  • Interactive plots:
    • Volcano Plot
    • MA Plot
    • Summary Bar Chart
    • Gene Expression Heatmap
  • Intelligent gene labeling with collision-free connectors
  • Multi-format export: PNG, PDF, TIFF, SVG at 600 DPI
  • Clean, two-step graphical user interface

💻 Installation & Usage

Option 1 – Standalone Executable (Recommended)

Download the file for your operating system from the Releases section:

Platform File Instructions
Windows DEG_Pipeline-Windows-x64.exe Double-click to run — no additional software required
macOS DEG_Pipeline-macOS.zip Unzip, then double-click DEG_Pipeline-macOS.app to run. You may need to right-click → "Open" the first time to bypass Gatekeeper
Linux DEG_Pipeline-Linux-x64 Make executable with chmod +x DEG_Pipeline-Linux-x64, then run with ./DEG_Pipeline-Linux-x64

Option 2 – Run from Source

# Clone the repository
git clone https://github.com/alirezabk1382927-sys/DEG-Pipeline-Visualizer.git
cd DEG-Pipeline-Visualizer

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the application
python deg_pipeline.py

🖥️ System Requirements

  • Windows 7 or later (64-bit recommended)
  • macOS 11 (Big Sur) or later
  • Linux (glibc-based distributions, 64-bit)
  • 4 GB RAM minimum (8 GB or more recommended for large datasets)

🚀 How to Use

  1. Launch the application.
  2. Load your data file (raw read counts / count matrix) and sample grouping metadata.
  3. Configure the analysis parameters (e.g., comparison group, p-value threshold, log2 fold change).
  4. Run the DEG analysis.
  5. View the generated plots and export them in your preferred format (PNG, PDF, TIFF, SVG).

⚠️ Known Issues

  • Large datasets (> 500 samples) may require additional memory.
  • The macOS build is unsigned; you may need to select "Open Anyway" under System Settings → Privacy & Security.

🤝 Contributing & Support

If you encounter any issues or have suggestions for improvement, please open a new Issue. Pull requests are welcome!

📄 License

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

✍️ Author

Alireza Balaei Kahnamoei

📌 Release History

See the Releases section for the full changelog of each version.

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