Incorporating biological information as a prior in an empirical bayes approach to analyzing microarray data

Currently the practice of using existing biological knowledge in analyzing high throughput genomic and proteomic data is mainly for the purpose of validations. Here we take a different approach of incorporating biological knowledge into statistical analysis to improve statistical power and efficiency. Specifically, we consider how to fuse biological information into a mixture model to analyze microarray data. In contrast to a standard mixture model where it is assumed that all the genes come from the same (marginal) distribution, including an equal prior probability of having an event, such as having differential expression or being bound by a transcription factor (TF), our proposed mixture model allows the genes in different groups to have different distributions while the grouping of the genes reflects biological information. Using a list of about 800 putative cell cycle-regulated genes as prior biological knowledge, we analyze a genome-wide location data to detect binding sites of TF Fkh1. We find that our proposal improves over the standard approach, resulting in reduced false discovery rates (FDR), and hence it is a useful alternative to the current practice.