Finding Common Modules in a Time-Varying Network with Application to the Drosophila Melanogaster Gene Regulation Network

Jingfei Zhang, Jiguo Cao

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Finding functional modules in gene regulation networks is an important task in systems biology. Many methods have been proposed for finding communities in static networks; however, the application of such methods is limited due to the dynamic nature of gene regulation networks. In this article, we first propose a statistical framework for detecting common modules in the Drosophila melanogaster time-varying gene regulation network. We then develop both a significance test and a robustness test for the identified modular structure. We apply an enrichment analysis to our community findings, which reveals interesting results. Moreover, we investigate the consistency property of our proposed method under a time-varying stochastic block model framework with a temporal correlation structure. Although we focus on gene regulation networks in our work, our method is general and can be applied to other time-varying networks. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)994-1008
Number of pages15
JournalJournal of the American Statistical Association
Volume112
Issue number519
DOIs
StatePublished - Jul 3 2017

Keywords

  • Community structure
  • Consistency
  • Gene regulation network
  • Hypothesis testing
  • Markov chain Monte Carlo
  • Robustness

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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