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

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.

We propose incorporating gene network information into statistical analysis of genomic data. Specifically, rather than treating the genes equally and independently a priori in a standard mixture model, we assume that gene-specific prior probabilities are correlated as induced by a gene network: while the genes are allowed to have different prior probabilities, those neighboring ones in the network have similar prior probabilities, reflecting their shared biological functions. We applied the two approaches to a real ChIP-chip dataset (and simulated data) to identify the transcriptional target genes of TF GCN4. The new method was found to be more powerful in discovering the target genes.