Research article

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

Xiangdong Liu^1,2 , Walter J Jessen² , Siva Sivaganesan³ , Bruce J Aronow² and Mario Medvedovic^1,2

¹  Department of Environmental Health, University of Cincinnati, 3223 Eden Ave. ML 56, Cincinnati, Ohio 45267, USA

²  Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA

³  Mathematical Sciences Department, University of Cincinnati, Cincinnati, OH 45221, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:283doi:10.1186/1471-2105-8-283

Published:	3 August 2007

Abstract

Background

Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental technologies, gene expression microarrays and Chromatin Immuno-Precipitation on Chip (ChIP-chip), are capable of producing data informative about expression regulatory mechanism on a genome scale. The optimal approach to joint modeling of data generated by these two complementary biological assays, with the goal of identifying and characterizing TMs, is an important open problem in computational biomedicine.

Results

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.

Conclusion

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.