PathIntegrate

PathIntegrate Python package for pathway-based multi-omics data integration

Features

• Pathway-based multi-omics data integration using PathIntegrate Multi-View and Single-View models
  • Multi-View model: Integrates multiple omics datasets using a shared pathway-based latent space
  • Single-View model: Integrates multi-omics data into one set of multi-omics pathway scores and applies an SKlearn-compatible predictive model
  • Pathway importance
  • Sample prediction
• SKlearn-like API for easy integration into existing pipelines
• Support for multiple pathway databases, including KEGG and Reactome
• Support for multiple pathway scoring methods available via the sspa package
• Cytoscape Network Viewer app for visualizing pathway-based multi-omics data integration results

Installation

pip install -i https://test.pypi.org/simple/ PathIntegrate

Tutorials and documentation

Please see our Quickstart guide on Google Colab

Citing PathIntegrate

If you use PathIntegrate in your research, please cite our paper:

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

Cecilia Wieder, Juliette Cooke, Clement Frainay, Nathalie Poupin, Jacob G. Bundy, Russell Bowler, Fabien Jourdan, Katerina J. Kechris, Rachel PJ Lai, Timothy Ebbels

Manuscript in preparation