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A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information
PLoS ONE (2012)
  • Erin M. Conlon, University of Massachusetts - Amherst
  • Bradley L. L. Postier
  • Barbara A. Methé
  • Kelly P. Nevin
  • Derek R. Lovley
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.
Publication Date
December 28, 2012
Citation Information
Erin M. Conlon, Bradley L. L. Postier, Barbara A. Methé, Kelly P. Nevin, et al.. "A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information" PLoS ONE Vol. 7 Iss. 12 (2012)
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Creative Commons license
Creative Commons License
This work is licensed under a Creative Commons CC_BY International License.