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.
NetColoc is available on PyPI
pip install netcoloc
Version 1.0.0 incorporates additional functionality developed for the publication Wright, S. N. et al., "Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks." Cell Reports (2023), as outlined at https://github.com/sarah-n-wright/CrossSpeciesBMI.
Prior version netcoloc v0.1.6 was utilized in the NetColoc publication: Rosenthal, S. B. et al., "Mapping the common gene networks that underlie related diseases." Nature Protocols (2023). To install this version, please use the following command:
pip install netcoloc==0.1.6
And follow the additional installation instructions at https://pypi.org/project/netcoloc/0.1.6/.
The original source code and example notebooks can be acquired from Zenodo: DOI:6654561, or from GitHub:
git clone git@github.com:ucsd-ccbb/NetColoc.git git checkout -b v0.1.6 tags/v0.1.6
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.
NetColoc requires the following python packages (automatically installed via pip install netcoloc)
- click >=6.0
- matplotlib
- ndex2
- networkx >=2.0,<3.0
- numpy
- seaborn
- tqdm
- mygene >= 3.2.2
- scipy >= 1.5.3
- statsmodels
- gprofiler-official >= 1.0.0
- ipywidgets
- ipycytoscape
- ipykernel
- obonet
- cdapsutil
- Free software: MIT license
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://doi.org/10.1038/s41596-022-00797-1
Other publications utilizing NetColoc:
- Rosenthal, S. B. et al. A convergent molecular network underlying autism and congenital heart disease. Cell Syst. 12, 1094-1107.e6 (2021). 10.1016/j.cels.2021.07.009
- Wright, S. N. et al. Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks. Cell Rep. 42, 112873 (2023). 10.1016/j.celrep.2023.112873
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.