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Meta-analytic framework for modeling genetic coexpression dynamics

Author

Listed:
  • Kinzy Tyler G.

    (Case Western Reserve University, Cleveland, USA)

  • Starr Timothy K.

    (University of Minnesota, Minneapolis, USA)

  • Tseng George C.

    (University of Pittsburgh, Pittsburgh, USA)

  • Ho Yen-Yi

    (Department of Statistics, University of South Carolina, Columbia, SC 29209, USA)

Abstract

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third “coordinator” gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

Suggested Citation

  • Kinzy Tyler G. & Starr Timothy K. & Tseng George C. & Ho Yen-Yi, 2019. "Meta-analytic framework for modeling genetic coexpression dynamics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(1), pages 1-13, February.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:1:p:13:n:1
    DOI: 10.1515/sagmb-2017-0052
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    References listed on IDEAS

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    1. Vân Anh Huynh-Thu & Alexandre Irrthum & Louis Wehenkel & Pierre Geurts, 2010. "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-10, September.
    2. Pedro A. Pérez-Mancera & Alistair G. Rust & Louise van der Weyden & Glen Kristiansen & Allen Li & Aaron L. Sarver & Kevin A. T. Silverstein & Robert Grützmann & Daniela Aust & Petra Rümmele & Thomas K, 2012. "The deubiquitinase USP9X suppresses pancreatic ductal adenocarcinoma," Nature, Nature, vol. 486(7402), pages 266-270, June.
    3. John A. Dawson & Christina Kendziorski, 2012. "An Empirical Bayesian Approach for Identifying Differential Coexpression in High-Throughput Experiments," Biometrics, The International Biometric Society, vol. 68(2), pages 455-465, June.
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