v3.4.0

🚀 DEG Pipeline & Visualizer — v3.4.0

Multi-Cancer Differential Gene Expression Analysis & Publication-Ready Visualization Suite

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📌 About This Release

This release delivers a polished, production-ready build of DEG Pipeline & Visualizer — a desktop application that takes raw TCGA/GDC RNA-seq count data all the way through PyDESeq2-powered differential expression analysis to publication-quality Volcano, MA, Bar Chart, and Heatmap visualizations, entirely through a modern graphical interface.

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✨ What's New in v3.4.0

• 🧬 Refined DEG analysis engine built on PyDESeq2, with improved handling of GDC Sample Sheets and automatic sample-group normalization.
• 🎨 Redesigned Scientific Dark theme alongside a clean Light theme, both optimized for long analysis sessions.
• 🌋 Four publication-ready plot types: Volcano Plot, MA Plot, Summary Bar Chart, and Expression Heatmap.
• 🏷️ Smarter gene labeling with automatic overlap avoidance via adjustText, plus optional labeled/unlabeled output pairs.
• 🖼️ Live multi-plot preview with thumbnail navigation before committing to export.
• 📤 Expanded export options — PNG, PDF, TIFF, and SVG at custom DPI, including a combined side-by-side composite figure.
• ⚡ Background multi-threaded processing to keep the interface responsive during long-running analyses, with a live progress log.
• 📚 Built-in guided tutorial and one-click citation generator, accessible directly from the About page.
• 🩺 Broad multi-cancer compatibility — works with any GDC/TCGA project (e.g. TCGA-COAD, TCGA-BRCA, TCGA-LUAD).

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💻 Downloads

Cross-platform support is provided for:

Platform	Status
🪟 Windows	✅ Supported
🍎 macOS	✅ Supported
🐧 Linux	✅ Supported

Download the build for your operating system from the assets attached to this release below, or run the application from source — see the installation guide in the project README.

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📦 Requirements

The complete, verified list of Python dependencies for this release is available in requirements.txt.

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📑 How to Cite This Version

Balaei A. (2026). DEG Pipeline & Visualizer (v3.4.0) [Computer software].
https://github.com/alirezabk1382927-sys/DEG-Pipeline-Visualizer

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📜 License Notice

Starting with this release, the project is distributed under a custom, restrictive, source-available proprietary license — replacing any previous MIT License. Copying, redistribution, or commercial use without explicit written permission from the Author is strictly prohibited. Full terms are available in LICENSE.

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🙏 Acknowledgements

Thank you to everyone using and supporting this project. If it helped your research, please ⭐ star the repository and cite the tool in your publications — it makes a real difference for continued development.

Full Changelog: View all commits

DEG Pipeline & Visualizer v2.0.4

DEG Pipeline & Visualizer

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

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📖 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.

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✨ 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

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💻 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

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🖥️ 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)

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🚀 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).

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⚠️ 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.

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🤝 Contributing & Support

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

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📄 License

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

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✍️ Author

Alireza Balaei Kahnamoei

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📌 Release History

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

DEG Pipeline & Visualizer v2.0.2

DEG Pipeline & Visualizer — First Public Release (v2.0.2)

We are excited to announce the first stable release of DEG Pipeline & Visualizer, an integrated desktop application for differential gene expression analysis and publication‑quality visualisation.

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What’s Included

This release provides a fully functional Windows executable (.exe) that requires no Python installation. Simply download, double‑click, and start analysing your RNA‑seq data.

Key Features

• Automated DEG analysis using PyDESeq2 (Tumor vs. Normal)
• Interactive volcano plots, MA plots, summary bar charts, and expression heatmaps
• Intelligent gene labelling with automatic collision‑free connectors
• Multi‑format export: PNG, PDF, TIFF, SVG at 600 DPI
• Clean, two‑step graphical user interface

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Installation & Usage

Option 1 – Standalone Executable (Recommended)

1. Download DEG_Pipeline.exe from the assets below.
2. Double‑click to run – no additional software required.

Option 2 – Run from Source

See the README for detailed instructions on setting up a Python environment and installing dependencies.

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System Requirements

• Windows 7 or later (64‑bit recommended)
• 4 GB RAM minimum (8 GB or more recommended for large datasets)

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Known Issues

• The executable is currently built for Windows only. macOS and Linux users should run from source.
• Large datasets (> 500 samples) may require additional memory; adjust accordingly.

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Feedback & Support

If you encounter any issues or have suggestions for improvement, please open an Issue on GitHub. We welcome contributions!

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Happy analysing!

Alireza Balaei Kahnamoei

Full Changelog: https://github.com/alirezabk1382927-sys/DEG-Pipeline-Visualizer/commits/v2.0.2