Hierarchical Mixture Models for Expression Profiles

Abstract

A class of probability models for inference about alterations in gene expression is reviewed. The class entails discrete mixing over patterns of equivalent and differential expression among different mRNA populations, continuous mixing over latent mean expression values conditional on each pattern, and variation of data conditional on latent means. An R package EBarrays implements inference calculations derived within this model class. The role of gene-specific probabilities of differential expression in the formation of calibrated gene lists is emphasized. In the context of the model class, differential expression is shown to be not just a shift in expected expression levels, but also an assertion about statistical independence of measurements from different mRNA populations. From this latter perspective, EBarrays is shown to be conservative in its assessment of differential expression.