Metadata-Version: 2.1
Name: PathIntegrate
Version: 0.0.3
Summary: PathIntegrate: multivariate modelling approaches for pathway-based muti-omics integration
Home-page: https://github.com/cwieder/PathIntegrate
Author: Cecilia Wieder
Author-email: cw2019@ic.ac.uk
License: GNU 3.0
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cmcrameri
Requires-Dist: dash
Requires-Dist: dash-bootstrap-components
Requires-Dist: dash-cytoscape
Requires-Dist: datauri
Requires-Dist: mbpls
Requires-Dist: networkx
Requires-Dist: setuptools
Requires-Dist: sspa >=1.0.1
Requires-Dist: statsmodels
Requires-Dist: svgwrite

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

![PathIntegrate graphical abstract](ModellingFrameworks_white.png "PathIntegrate graphical abstract")

#### 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](https://github.com/cwieder/py-ssPA) package
- Cytoscape Network Viewer app for visualizing pathway-based multi-omics data integration results

![PathIntegrate Cytoscape app](App_network_view.jpg "Network viewer")

## Installation
```bash
pip install -i https://test.pypi.org/simple/ PathIntegrate
```

## Tutorials and documentation
Please see our Quickstart guide on [Google Colab](https://colab.research.google.com/drive/1nv9lp8mMQ2Yk8n9uI9hBMvH71MlWp3UJ?usp=sharing)

Full documentation and function reference for PathIntegrate can be found via our [ReadTheDocs page](https://cwieder.github.io/PathIntegrate/)

## Citing PathIntegrate
If you use PathIntegrate in your research, please cite our paper:
```bibtex
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
```
