Bayesian Methods for Microarray Data

We review the use of Bayesian methods for analyzing gene expression data. We focus on methods which select groups of genes on the basis of their expression in RNA samples derived under different experimental conditions. We first describe Bayesian methods for estimating gene expression level from the intensity measurements obtained from analysis of microarray images. We next discuss the issues involved in assessing differential gene expression between two conditions at a time, including models for classifying the genes as differentially expressed or not. In the last two sections, we present models for grouping gene expression profiles over different experimental conditions, in order to find co-expressed genes, and multivariate models for finding gene signatures, i.e. for selecting a parsimonious group of genes that discriminate between entities such as subtypes of disease.

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