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Meta-analytic framework for modeling genetic coexpression dynamics.

Tyler G Kinzy1, Timothy K Starr2, George C Tseng3

  • 1Case Western Reserve University, Cleveland, USA.

Statistical Applications in Genetics and Molecular Biology
|February 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a meta-analytic framework for liquid association (LA) to analyze gene coexpression dynamics. The new Bayesian hierarchical model accounts for study heterogeneity, improving the understanding of gene regulatory networks.

Keywords:
bayesian hierarchical modelgene coexpression analysisgene coexpression dynamicsliquid associationmeta-analysis

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene coexpression patterns change under different cellular conditions, revealing regulatory mechanisms.
  • Liquid association (LA) quantifies how a third gene (coordinator) modulates coexpression between two genes.
  • Existing methods lack a meta-analytic framework for analyzing gene coexpression dynamics across studies.

Purpose of the Study:

  • To develop a meta-analytic framework for liquid association (LA) analysis.
  • To incorporate mixed effects modeling to address between-study heterogeneity in gene expression data.
  • To apply a Bayesian hierarchical framework with Markov chain Monte Carlo (MCMC) estimation for statistical inference.

Main Methods:

  • Developed a meta-analytic framework for liquid association (LA).
  • Incorporated mixed effects modeling to account for between-study heterogeneity.
  • Utilized a Bayesian hierarchical framework with Markov chain Monte Carlo (MCMC) estimation for statistical inference.

Main Results:

  • Evaluated the proposed methods through simulations.
  • Applied the framework to analyze USP9X in pancreatic cancer gene expression data.
  • Examined coexpression with serA in Escherichia coli using 907 microarray experiments.

Conclusions:

  • The developed meta-analytic framework provides a robust approach for analyzing gene coexpression dynamics.
  • The methods effectively handle between-study heterogeneity in large-scale gene expression data.
  • Demonstrated utility in identifying potential coordinator genes in human cancer and bacterial regulatory networks.