PathIntegrate 0.0.3

PathIntegrate: multivariate modelling approaches for pathway-based muti-omics integration

PathIntegrate

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

Abstract

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway.

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

Full documentation and function reference for PathIntegrate can be found via our ReadTheDocs page

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