pathway-subtyping 0.2.1

A disease-agnostic framework for identifying molecular subtypes through pathway-based analysis of rare genetic variants

Pathway Subtyping Framework

A Disease-Agnostic Tool for Pathway-Based Molecular Subtype Discovery

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Overview

The Pathway Subtyping Framework is an open-source computational tool for identifying molecular subtypes in genetically heterogeneous diseases. Instead of analyzing individual genes, it aggregates rare variant burden at the biological pathway level, enabling:

• Better signal detection across genetically diverse cohorts
• Identification of biologically coherent patient subgroups
• Cross-cohort validation of discovered subtypes

Originally developed for autism research, this generalized version can be adapted for any disease with:

• Genetic heterogeneity (many implicated genes)
• Convergent pathway biology
• Available exome/genome sequencing data

Supported Disease Areas

Disease	Status	Pathway File
Autism Spectrum Disorder	Validated	autism_pathways.gmt
Schizophrenia	Template	schizophrenia_pathways.gmt
Epilepsy	Template	epilepsy_pathways.gmt
Intellectual Disability	Template	intellectual_disability_pathways.gmt
Parkinson's Disease	Template	parkinsons_pathways.gmt
Bipolar Disorder	Template	bipolar_pathways.gmt
Your disease	Adapt it →	your_pathways.gmt

Key Features

Feature	Description
Pathway Scoring	Aggregate gene burdens across biological pathways
Multiple Clustering	GMM, K-means, Hierarchical, Spectral with cross-validation
Ancestry Correction	PCA-based population stratification correction with independence testing
Batch Correction	ComBat-style batch effect detection and correction
Sensitivity Analysis	Parameter robustness testing across algorithms, features, normalization
Validation Gates	Negative controls + bootstrap stability + ancestry independence testing
Statistical Rigor	FDR correction, effect sizes, confidence intervals
Power Analysis	Sample size recommendations, Type I error estimation
Simulation	Synthetic data generation with ground truth for validation
Reproducibility	Deterministic execution, pinned dependencies, Docker
Config-Driven	YAML configuration for all parameters

Quick Start

Installation

# Clone the repository
git clone https://github.com/topmist-admin/pathway-subtyping-framework
cd pathway-subtyping-framework

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install the package
pip install -e .

# Verify installation
psf --version

Run with Sample Data

# Run the pipeline with synthetic test data
psf --config configs/test_synthetic.yaml

# View results
cat outputs/synthetic_test/report.md

Run with Your Data

# Copy and customize a config
cp configs/example_autism.yaml configs/my_analysis.yaml

# Edit paths in my_analysis.yaml, then run
psf --config configs/my_analysis.yaml

Try in Browser (No Installation)

Docker

# Run pipeline
docker-compose run pipeline

# Run tests
docker-compose run test

# Start Jupyter notebook
docker-compose up jupyter
# Open http://localhost:8888

Adapting for Your Disease

1. Create a pathway GMT file with disease-relevant gene sets
2. Copy an example config and point to your data
3. Run the pipeline — validation gates will tell you if subtypes are meaningful

See the full guide: Adapting for Your Disease

How It Works

VCF Input → Variant Filter → Gene Burden → Pathway Aggregation → [Ancestry Correction] → [Batch Correction] → GMM Clustering → [Sensitivity Analysis] → Validation → Report

1. Pathway Scoring

Rare damaging variants are aggregated into pathway-level disruption scores:

• Loss-of-function variants weighted higher
• Missense variants weighted by CADD score
• Scores normalized across samples

2. Subtype Discovery

Multiple clustering algorithms identify patient subgroups:

• GMM (default): Soft assignments, automatic selection via BIC
• K-means: Fast, spherical clusters
• Hierarchical: Dendogram-based, no K required
• Spectral: Nonlinear boundaries
• Cross-validation for stability assessment
• Algorithm comparison with pairwise ARI

3. Validation Gates

Built-in tests prevent overfitting:

• Label shuffle: Randomized labels should NOT cluster (ARI < 0.15)
• Random genes: Fake pathways should NOT work (ARI < 0.15)
• Bootstrap: Clusters should be stable under resampling (ARI > 0.8)
• Ancestry independence: Clusters should not correlate with ancestry PCs (when provided)

4. Statistical Rigor

Publication-quality statistics:

• FDR correction: Benjamini-Hochberg for multiple testing
• Effect sizes: Cohen's d with 95% bootstrap confidence intervals
• Power analysis: Sample size recommendations for target effect sizes
• Type I error: Estimation via null simulations

See docs/METHODS.md for full statistical methodology.

Data Requirements

Input	Format	Notes
Variants	VCF	Annotated with gene symbols, consequences
Phenotypes	CSV	Sample IDs + clinical features
Pathways	GMT	Gene sets for your disease

Your data stays on your infrastructure. The framework runs locally or in your cloud environment.

Project Structure

pathway-subtyping-framework/
├── src/pathway_subtyping/     # Core Python package
│   ├── pipeline.py            # Main pipeline
│   ├── clustering.py          # Multiple clustering algorithms
│   ├── statistical_rigor.py   # FDR, effect sizes, burden weights
│   ├── simulation.py          # Synthetic data & power analysis
│   ├── validation.py          # Validation gates
│   ├── ancestry.py            # Population stratification correction
│   ├── batch_correction.py    # Batch effect detection & correction
│   ├── sensitivity.py         # Parameter sensitivity analysis
│   └── data_quality.py        # VCF quality checks
├── configs/                   # Example YAML configurations
├── data/
│   ├── pathways/              # Pathway GMT files (6 diseases)
│   └── sample/                # Synthetic test data
├── docs/
│   ├── METHODS.md             # Statistical methods documentation
│   └── guides/                # User guides
├── examples/notebooks/        # Jupyter tutorials
├── tests/                     # Test suite (347 tests)
├── Dockerfile                 # Container support
└── docker-compose.yml         # Easy orchestration

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run linting
black src/ tests/
isort src/ tests/
flake8 src/ tests/

# Set up pre-commit hooks
pre-commit install

Related Projects

• Autism Pathway Framework — The original autism-focused implementation with SFARI cohort validation

Contributing

Contributions welcome! Areas where help is needed:

• Additional disease pathway definitions
• Performance optimization for large cohorts
• Documentation and tutorials

See CONTRIBUTING.md for guidelines.

Citation

If you use this framework, please cite:

Chauhan R. Pathway Subtyping Framework. GitHub. 2026.
https://github.com/topmist-admin/pathway-subtyping-framework

For autism-specific work, also cite:

Chauhan R. Autism Pathway Framework. Zenodo. 2026.
DOI: 10.5281/zenodo.18403844

License

MIT License — see LICENSE for details.

Contact

Rohit Chauhan

• Email: [e-mail]
• GitHub: @topmist-admin

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RESEARCH USE ONLY — This framework is for hypothesis generation. Not for clinical diagnosis or treatment decisions.