pathway-subtyping 0.1.0

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
Your disease	Adapt it →	your_pathways.gmt

Key Features

Feature	Description
Pathway Scoring	Aggregate gene burdens across biological pathways
Subtype Discovery	GMM clustering with automatic model selection (BIC)
Validation Gates	Negative controls + bootstrap stability testing
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 → GMM Clustering → 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

Gaussian Mixture Model clustering identifies patient subgroups:

• Automatic cluster selection via BIC
• Configurable cluster range (default: 2-8)

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)

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
├── configs/                   # Example YAML configurations
│   ├── example_autism.yaml
│   ├── example_schizophrenia.yaml
│   ├── test_synthetic.yaml    # Ready-to-run test config
│   └── example_epilepsy.yaml
├── data/
│   ├── pathways/              # Pathway GMT files
│   └── sample/                # Synthetic test data
├── docs/guides/               # Documentation
│   ├── adapting-for-your-disease.md
│   ├── pathway-curation-guide.md
│   └── validation-gates.md
├── examples/notebooks/        # Jupyter tutorials
├── tests/                     # Test suite (64 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.