Netmix: a network-structured mixture model for reduced-bias estimation of altered subnetworks

Matthew A. Reyna, Uthsav Chitra, Rebecca Elyanow, Benjamin J. Raphael

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 to 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 which 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. 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.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
EditorsRussell Schwartz
PublisherSpringer
Pages169-185
Number of pages17
ISBN (Print)9783030452568
DOIs
StatePublished - Jan 1 2020
Event24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy
Duration: May 10 2020May 13 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12074 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020
CountryItaly
CityPadua
Period5/10/205/13/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Cancer
  • Gene expression
  • Interaction networks
  • Maximum likelihood estimation
  • Network anomaly

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  • Cite this

    Reyna, M. A., Chitra, U., Elyanow, R., & Raphael, B. J. (2020). Netmix: a network-structured mixture model for reduced-bias estimation of altered subnetworks. In R. Schwartz (Ed.), Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings (pp. 169-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12074 LNBI). Springer. https://doi.org/10.1007/978-3-030-45257-5_11