PathCORE

Python 3 implementation of methods described in Chen et al.'s 2017 PathCORE paper for identifying pathway-pathway interactions using features constructed from transcriptomic data.

This code has been tested on Python 3.5.

Installation

To install the current PyPI version (recommended), run:

pip install PathCORE

For the latest GitHub version, run:

pip install git+https://github.com/greenelab/PathCORE.git#egg=PathCORE

Package contents

feature_pathway_overrepresentation.py

The methods in this module are used to identify the pathways overrepresented in features extracted from a transcriptomic dataset of genes-by-samples. Features must preserve the genes in the dataset and assign weights to these genes based on some distribution.

network.py

Contains the data structure CoNetwork that stores information about the pathway co-occurrence network. The output from a pathway enrichment analysis in feature_pathway_overrepresentation.py serves as input into the CoNetwork constructor.

network_permutation_test.py

The methods in this module are used to filter the constructed co-occurence network. We implement a permutation test that evaluates and removes edges (pathway-pathway relationships) in the network that cannot be distinguished from a null model of random associations. The null model is created by generating N permutations of the network.

Acknowledgements

This work was supported by the Penn Institute for Bioinformatics