Statistical methods in integrative analysis for gene regulatory modules

PI: Dr. Jun Xie
Funding: NSF DMS-604776
Other people: Lingmin Zeng, Department of Statistics, Purdue University and Dr. Jing Wu, Department of Statistics, Carnegie Mellon University.


A suite of statistical methods are proposed for inferring 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, a hidden Markov model is developed 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, factor analysis is particularly applied, whose statistical model characterizes the modular structure of cis-regulation. When groups of coexpressed genes are available, canonical correlation analysis is further applied to infer relationships between a group of genes and their common set of transcription factors. 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.


Lingmin Zeng, Jing Wu, and Jun Xie, "Statistical methods in integrative analysis for gene regulatory modules", Statistical Applications in Genetics and Molecular Biology, 2008, Vol. 7, Iss. 1, Article 28,

Jing Wu and Jun Xie, "Computation-Based Discovery of Cis-Regulatory Modules by Hidden Markov Models", Journal of Computational Biology, 2008, Vol. 15, No. 3, 279-290.

Last Updated: Sep 18, 2017 1:15 PM

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