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 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