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Published Online: 20 May 2021

NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks

Publication: Journal of Computational Biology
Volume 28, Issue Number 5

Abstract

A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.

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Published In

cover image Journal of Computational Biology
Journal of Computational Biology
Volume 28Issue Number 5May 2021
Pages: 469 - 484
PubMed: 33400606

History

Published online: 20 May 2021
Published in print: May 2021
Published ahead of print: 5 January 2021

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Data Availability

NetMix is available online at https://github.com/raphael-group/netmix

Authors

Affiliations

Matthew A. Reyna*
Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA.
Uthsav Chitra*
Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
Rebecca Elyanow
Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
Department of Computer Science, Brown University, Providence, Rhode Island, USA.
Benjamin J. Raphael [email protected]
Department of Computer Science, Princeton University, Princeton, New Jersey, USA.

Notes

*
These authors contributed equally.
Address correspondence to: Prof. Benjamin J. Raphael, Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA [email protected]

Author Disclosure Statement

B.J.R. is a cofounder of, and consultant to, Medley Genomics.

Funding Information

M.A.R. was supported in part by the National Cancer Institute of the NIH (Cancer Target Discovery and Development Network grant U01CA217875). B.J.R. was supported by US National Institutes of Health (NIH) grants R01HG007069 and U24CA211000.

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