Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data

We developed and validated a novel probabilistic model and related computational procedure for identifying TMs by jointly modeling gene expression and ChIP-chip binding data. We demonstrate an improved functional coherence of the TMs produced by the new method when compared to either analyzing expression or ChIP-chip data separately or to alternative approaches for joint analysis. We also demonstrate the ability of the new algorithm to identify novel regulatory relationships not revealed by ChIP-chip data alone. The new computational procedure can be used in more or less the same way as one would use simple hierarchical clustering without performing any special transformation of data prior to the analysis. The R and C-source code for implementing our algorithm is incorporated within the R package gimmR which is freely available at http://eh3.uc.edu/gimm.Our results indicate that, whenever available, ChIP-chip and expression data should be analyzed within the unified probabilistic modeling framework, which will likely result in improved clusters of co-regulated genes and improved ability to detect meaningful regulatory relationships. Given the good statistical properties and the ease of use, the new computational procedure offers a worthy new tool for reconstructing transcriptional regulatory networks.Transcriptional regula tion is one of the crucial mechanisms used by living systems to maintain homeostasis. Disregulation of gene expression underlies toxic effects of many chemicals [1], and gene expression changes are often reliable markers of a disease [2]. The specificity of transcriptional initiation of a eukaryotic gene is maintained through a complex interaction of one or more sequence-specific transcription factors, regulatory DNA regions harboring corresponding DNA regulatory motifs, chromatin-remodeling proteins and the basal transcriptional machinery [3]. While not all modes of expression regulatory controls are known, it has been shown that in many importan