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

Methodology