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