Epistatic miniarray profile (E-MAP) is a popular large-scale gene interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions. However, due to the limits of biotechnology, E-MAP studies fail to measure genetic interactions for up to 40% of gene pairs in an assay.
Network-guided matrix completion (NG-MC) is a knowledge-assisted method for imputing and predicting interactions in E-MAP-like data sets. The core part of NG-MC is low-rank probabilistic matrix completion that considers additional knowledge presented as a collection of gene networks.
NG-MC assumes that interactions are transitive, such that latent gene interaction profiles inferred by NG-MC depend on the profiles of their direct neighbors in gene networks. As the NG-MC inference algorithm progresses, it propagates latent interaction profiles through each of the networks and updates gene network weights towards improved prediction.
This repository contains supplementary material for Data imputation in epistatic MAPs by network-guided matrix completion by Marinka Zitnik and Blaz Zupan.
Fitting network-guided matrix completion with default parameters::
>>> from ngmc import Ngmc
>>> from data import loader
>>> G, gene2idx = loader.load_surma_emap("data/surma-mmc7/S-Scores-lipid-E-MAP.csv")
>>> ngmc = Ngmc(G, c=60)
>>> ngmc.fit()
For complete example see example.py or run::
$ python example.py
@article{Zitnik2015,
title = {Data imputation in epistatic {MAP}s by network-guided matrix completion},
author = {{\v{Z}}itnik, Marinka and Zupan, Bla{\v{z}}},
journal = {Journal of Computational Biology},
volume = {22},
number = {6},
pages = {595-608},
year = {2015},
publisher = {Mary Ann Liebert, Inc}
}