PathwayLens - Research-Grade Pathway Enrichment Analysis

Professional computational biology tool for pathway enrichment analysis across bulk RNA-seq, single-cell RNA-seq, ATAC-seq, proteomics, and multi-omics datasets.

Installation

pip install pathwaylens

See INSTALL.md for detailed instructions.

Quick Start

Basic Usage

1. Analyze a gene list (ORA):

pathwaylens analyze ora \
    --input genes.txt \
    --omic-type transcriptomics \
    --data-type bulk \
    --databases kegg \
    --species human \
    --output-dir results_ora

2. Analyze ranked genes (GSEA):

pathwaylens analyze gsea \
    --input ranked_genes.rnk \
    --omic-type transcriptomics \
    --data-type bulk \
    --databases kegg \
    --species human \
    --output-dir results_gsea

3. Compare datasets:

pathwaylens compare \
    --inputs list1.txt \
    --inputs list2.txt \
    --mode genes \
    --omic-type transcriptomics \
    --data-type bulk \
    --output-dir comparison_results

For detailed documentation, see CLI Reference.

Core Commands

• analyze ora - Over-Representation Analysis with hypergeometric test
• analyze gsea - Gene Set Enrichment Analysis for ranked lists
• compare - Compare multiple gene lists or pathway results
• normalize - Gene ID conversion across formats

Key Features

• Intelligent Tool Detection: Automatically detects DESeq2, edgeR, limma, MaxQuant formats
• Multiple Databases: KEGG, Reactome, GO, WikiPathways, MSigDB support
• Research-Grade Statistics: Odds ratios, effect sizes, confidence intervals
• Publication-Quality Plots: Interactive and static visualizations
• Cross-Species Support: Human, mouse, rat, and more
• Single-Cell Ready: Native sparse matrix support

Important

Single-Cell Normalization: PathwayLens assumes input scRNA-seq data (.h5ad, .csv) is already normalized for sequencing depth (e.g., LogNormalize, SCTransform). It does not perform library size normalization internally. Using raw counts will lead to invalid results.

Documentation

• Installation: INSTALL.md
• CLI Reference: docs/CLI_REFERENCE.md
• Input Formats: docs/user_guide/
• Examples: examples/

Validation

# Verify installation
python scripts/validation/validate_installation.py

# Run tests
pytest tests/

Citation

@software{pathwaylens2025,
  title={PathwayLens: Research-Grade Pathway Enrichment Analysis},
  author={PathwayLens Contributors},
  year={2025},
  version={1.0.0},
  url={https://github.com/VibhavSetlur/PathwayLens}
}

License

MIT License - see LICENSE

Support

• Issues: GitHub Issues
• Documentation: Complete guides in docs/