deg-pathway-enrichment-tool 1.0.0

A comprehensive tool for differential gene expression pathway enrichment analysis

DEG Pathway Enrichment Tool

A comprehensive Python package for differential gene expression (DEG) pathway enrichment analysis. This tool provides an easy-to-use interface for analyzing gene expression data and identifying enriched biological pathways.

Features

• 🧬 Comprehensive Pathway Analysis: Supports multiple databases (KEGG, GO Biological Process, Reactome, MSigDB Hallmark)
• 📊 Rich Visualizations: Static and interactive plots with customizable DPI
• 🔍 Gene Family Analysis: Specialized analysis for keratin, claudin, and other gene families
• 📈 Multiple Plot Types: Bar plots, dot plots, volcano plots, and comprehensive summaries
• 📝 Detailed Reports: Markdown reports with analysis summaries
• 🎯 Flexible Thresholds: Customizable log fold change and p-value cutoffs
• 💻 Command-line Interface: Easy-to-use CLI for batch processing

Installation

From PyPI (Recommended)

pip install deg-pathway-enrichment-tool

From Source

git clone https://github.com/yourusername/deg-pathway-enrichment-tool.git
cd deg-pathway-enrichment-tool
pip install -e .

Quick Start

Command Line Usage

# Basic analysis
deg-pathway-analysis your_deg_data.csv

# Custom output directory and thresholds
deg-pathway-analysis your_deg_data.csv -o results/ --logfc-threshold 2.0 --pval-threshold 0.001

# High-resolution figures
deg-pathway-analysis your_deg_data.csv --dpi 1200

Python API Usage

from deg_pathway_enrichment_tool import DEGPathwayAnalyzer

# Initialize analyzer
analyzer = DEGPathwayAnalyzer(
    deg_file="your_deg_data.csv",
    output_dir="./results",
    logfc_threshold=1.5,
    pval_threshold=0.01,
    dpi=600
)

# Run complete analysis
analyzer.run_complete_analysis()

Input Data Format

Your CSV file must contain the following columns:

Column Name	Description	Example
names	Gene names/symbols	GAPDH, TP53, MYC
logfoldchanges	Log fold change values	2.5, -1.8, 3.2
pvals_adj	Adjusted p-values	0.001, 0.05, 1e-10

Example CSV format:

names,logfoldchanges,pvals_adj
KRT7,8.72,1e-300
CLDN10,8.52,3.35e-197
TP53,-2.1,0.001
GAPDH,1.2,0.05

Output Files

The tool generates comprehensive results including:

Visualizations

• pathway_barplot.png - Bar plot of top enriched pathways
• pathway_dotplot.png - Dot plot showing pathway significance vs effect size
• interactive_pathway_barplot.html - Interactive pathway visualization
• keratin_expression.png - Keratin gene family analysis
• claudin_expression.png - Claudin gene family analysis
• comprehensive_summary.png - Multi-panel summary figure

Data Files

• pathway_enrichment_results.csv - Complete pathway enrichment results
• keratin_genes.csv - Keratin gene analysis results
• claudin_genes.csv - Claudin gene analysis results
• analysis_report.md - Comprehensive analysis report

Command Line Options

deg-pathway-analysis --help

Option	Description	Default
input_file	Path to CSV file containing DEG results	Required
-o, --output-dir	Output directory for results	./deg_analysis_results
--logfc-threshold	Log fold change threshold for significance	1.5
--pval-threshold	Adjusted p-value threshold for significance	0.01
--dpi	DPI for saved figures	600
--databases	Pathway databases to use	KEGG, GO-BP, Reactome, MSigDB

Supported Pathway Databases

• KEGG_2021_Human: KEGG pathway database
• GO_Biological_Process_2021: Gene Ontology Biological Process
• Reactome_2022: Reactome pathway database
• MSigDB_Hallmark_2020: MSigDB Hallmark gene sets

Advanced Usage

Custom Database Selection

deg-pathway-analysis input.csv --databases KEGG_2021_Human GO_Biological_Process_2021

Python API - Step by Step

from deg_pathway_enrichment_tool import DEGPathwayAnalyzer

# Initialize
analyzer = DEGPathwayAnalyzer("data.csv", output_dir="results")

# Run individual steps
pathway_results = analyzer.run_pathway_enrichment()
family_results = analyzer.analyze_gene_families()
analyzer.create_pathway_plots()
analyzer.create_comprehensive_summary()
analyzer.generate_report()

Requirements

• Python ≥ 3.8
• pandas ≥ 2.0.0
• numpy ≥ 1.24.0
• matplotlib ≥ 3.7.0
• seaborn ≥ 0.12.0
• plotly ≥ 5.15.0
• gseapy ≥ 1.0.4
• scipy ≥ 1.10.0

Publishing to PyPI

1. Prepare Your Package

Ensure your package structure is correct and all files are in place.

2. Build the Package

cd deg-pathway-enrichment-tool
pip install build twine
python -m build

3. Upload to PyPI

# Test PyPI first (recommended)
python -m twine upload --repository testpypi dist/*

# Production PyPI
python -m twine upload dist/*

4. GitHub Integration

1. Create a GitHub repository
2. Push your code:

git init
git add .
git commit -m "Initial commit"
git branch -M main
git remote add origin https://github.com/yourusername/deg-pathway-enrichment-tool.git
git push -u origin main

3. Set up GitHub Actions for automated PyPI publishing (optional):
   • Create .github/workflows/publish.yml
   • Add PyPI API token to GitHub secrets

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Citation

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

DEG Pathway Enrichment Tool (2025). 
Available at: https://github.com/yourusername/deg-pathway-enrichment-tool

Support

• 📖 Documentation: GitHub Wiki
• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: GitHub Discussions

Changelog

v1.0.0 (2025-07-30)

• Initial release
• Comprehensive pathway enrichment analysis
• Multiple visualization options
• Gene family analysis
• Command-line interface
• Python API