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NetColoc

Documentation Status

Description

Here we introduce NetColoc, a tool which evaluates the extent to which two gene sets are related in network space, i.e. the extent to which they are colocalized in a molecular interaction network, and interrogates the underlying biological pathways and processes using multiscale community detection.

This framework may be applied to any number of scenarios in which gene sets have been associated with a phenotype or condition, including rare and common variants within the same disease, genes associated with two comorbid diseases, genetically correlated GWAS phenotypes, GWAS across two different species, or gene expression changes after treatment with two different drugs, to name a few.

NetColoc relies on a dual network propagation approach to identify the region of network space which is significantly proximal to both input gene sets, and as such is highly effective for small to medium input gene sets.

Documentation

For a quick-start on NetColoc's functionality, please see the example notebooks.

Usage Note: Please follow steps in example notebooks for correct usage of NetColoc. At this time, individual functionalities have not been tested for independent use.

Dependencies

NetColoc requires the following python packages:

Note

All of the following packages minus DDOT and cdapsutil will be automatically installed via pip install netcoloc

Additional requirements for full functionality of example notebook:

Installation

NetColoc is available on PyPI

pip install netcoloc

License

  • Free software: MIT license

Citing NetColoc

Rosenthal, Sara Brin, Sarah N. Wright, Sophie Liu, Christopher Churas, Daisy Chilin-Fuentes, Chi-Hua Chen, Kathleen M. Fisch, Dexter Pratt, Jason F. Kreisberg, and Trey Ideker. "Mapping the common gene networks that underlie related diseases." Nature protocols (2023): 1-15.

https://www.nature.com/articles/s41596-022-00797-1

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.