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Bayesian Mixture Models for Gene Expression and Protein Profiles

Michele Guindani, The University of Texas M.D. Anderson Cancer Center
Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center
Peter Mueller, The University of Texas M.D. Anderson Cancer Center
Jeffrey S. Morris, The University of Texas M.D. Anderson Cancer Center

Abstract

We review the use of semi-parametric mixture models for Bayesian inference in high throughput genomic data. We discuss three specific approaches for microarray data, for protein mass spectrometry experiments, and for SAGE data. For the microarray data and the protein mass spectrometry we assume group comparison experiments, i.e., experiments that seek to identify genes and proteins that are differentially expressed across two biologic conditions of interest. For the SAGE data example we consider inference for a single biologic sample.

Suggested Citation

Michele Guindani, Kim-Anh Do, Peter Mueller, and Jeffrey S. Morris. "Bayesian Mixture Models for Gene Expression and Protein Profiles" Bayesian Inference for Gene Expression and Proteomics. Ed. KA Do, P Mueller, M Vannucci. New York: Cambridge University Press, 2006. 238-253.
Available at: http://works.bepress.com/jeffrey_s_morris/16

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