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

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  • Jingfei Zhang
  • Jiguo Cao

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.

Suggested Citation

  • Jingfei Zhang & Jiguo Cao, 2017. "Finding Common Modules in a Time-Varying Network with Application to the Gene Regulation Network," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 994-1008, July.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:519:p:994-1008
    DOI: 10.1080/01621459.2016.1260465
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    1. Nam P Nguyen & Thang N Dinh & Yilin Shen & My T Thai, 2014. "Dynamic Social Community Detection and Its Applications," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-18, April.
    2. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
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    Cited by:

    1. Wei Hu & Tianyu Pan & Dehan Kong & Weining Shen, 2021. "Nonparametric matrix response regression with application to brain imaging data analysis," Biometrics, The International Biometric Society, vol. 77(4), pages 1227-1240, December.

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