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The standard methods for detecting differential gene expression are mostlydesigned for analyzing a single gene expression experiment. When data frommultiple related gene expression studies are available, separately analyzingeach study is not an ideal strategy as it may fail to detect important geneswith consistent but relatively weak differential signals in multiple studies.Jointly modeling all data allows one to borrow information across studies toimprove the analysis. However, a simple concordance model, in which each geneis assumed to be differential in either all studies or none of the studies, isincapable of handling genes with study-specific differential expression. Incontrast, a model that naively enumerates and analyzes all possibledifferential patterns across all studies can deal with study-specificity andallow information pooling, but the complexity of its parameter space growsexponentially as the number of studies increases. Here we propose a"correlation motif" approach to address this dilemma. This approachautomatically searches for a small number of latent probability vectors called"correlation motifs" to capture the major correlation patterns among multiplestudies. The motifs provide the basis for sharing information among studies andgenes. The approach improves detection of differential expression and overcomesthe barrier of exponentially growing parameter space. It is capable of handlingall possible study-specific differential patterns in a large number of studies.The advantages of this new approach over existing methods are illustrated usingboth simulated and real data.

Joint Analysis of Differential Gene Expression in Multiple Studies using Correlation Motifs