Statistical methods in integrative analysis for gene regulatory modules.

We propose a suite of statistical methods for inferring a cis-regulatory module, which is a combination of several transcription factors binding in the promoter regions to regulate gene expression. The approach is an integrative analysis that combines information from multiple types of biological data, including genomic DNA sequences, genome-wide location analysis (ChIP-chip experiments), and gene expression microarray. More specifically, we use a hidden Markov model to first predict a cluster of transcription factor binding sites in DNA sequences. The predictions are refined by regression analysis on gene expression microarray data and/or ChIP-chip binding experiments. In regression analysis, we particularly apply factor analysis, whose statistical model characterizes the modular structure of cis-regulation. When groups of coexpressed genes are available, we further apply canonical correlation analysis to infer relationships between a group of genes and their common set of transcription factors. Our approach is validated on the well-studied yeast cell cycle gene regulation. It is then used to study condition-specific regulators for a set of Ste12 target genes. The multiple data sources provide information of transcriptional regulation from different aspects. Therefore, the integrative analysis offers a fine prediction on transcriptional regulatory code and infers potential regulatory networks.