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It is a common task in genomic studies to identify a subset of the genes satisfying certain conditions, such as differentially expressed genes or regulatory target genes of a transcription factor (TF). This can be formulated as a statistical hypothesis testing problem. Most existing approaches treat the genes as having an identical and independent distribution a priori, testing each gene independently or testing some subsets of the genes one by one. On the other hand, it is known that the genes work coordinately as dictated by gene networks. Treating genes equally and independently ignores the important information contained in gene networks, leading to inefficient analysis and reduced power.

Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model