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A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information

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  • Erin M Conlon
  • Bradley L Postier
  • Barbara A Methé
  • Kelly P Nevin
  • Derek R Lovley

Abstract

Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. We assume that each study contains two experimental conditions: a treatment and control. We note that there exist environmental conditions for which genes that are supposed to be transcribed together lose their operon structure, and that our model is best carried out for known operon structures.

Suggested Citation

  • Erin M Conlon & Bradley L Postier & Barbara A Methé & Kelly P Nevin & Derek R Lovley, 2012. "A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    • Handle: RePEc:plo:pone00:0052137
    DOI: 10.1371/journal.pone.0052137
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    References listed on IDEAS

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    1. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
    2. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
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    Cited by:

    1. Steffen Ventz & Rahul Mazumder & Lorenzo Trippa, 2022. "Integration of survival data from multiple studies," Biometrics, The International Biometric Society, vol. 78(4), pages 1365-1376, December.

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