Statistical Methods for Inference of Genetic Networks and Regulatory Modules

Abstract

Large-scale microarray gene expression data, motif data derived from promotor sequences, genome-wide chromatin immunoprecipitation (ChIP-chip) data, DNA polymorphism data and epigenomic data provide the possibility of constructing genetic networks or biological pathways, especially regulatory networks. In this paper, we review some new statistical methods for inference of genetic networks and regulatory modules, including a threshold gradient descent procedure for inference of Gaussian graphical models, a sparse regression mixture modeling approach for inference of regulatory modules, and the varying coefficient model for identifying regulatory subnetworks by integrating microarray time-course gene expression data and motif or ChIP-chip data. We present the statistical formulations of the problems, statistical methods, and results from analysis of real data sets. Areas of future research are also discussed. Statistical Methods for Inference of Genetic Networks and Regulatory Modules A Chapter in “Analysis of Microarray Data” edited by Dehmer and Emmert-Streib, to be published by Wiley-VCH. Hongzhe Li Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, U.S.A. ∗email: hongzhe@mail.med.upenn.edu Abstract Large-scale microarray gene expression data, motif data derived from promotor sequences, genome-wide chromatin immunoprecipitation (ChIP-chip) data, DNA polymorphism data and epigenomic data provide the possibility of constructing genetic networks or biological pathways, especially regulatory networks. In this paper, we review some new statistical methods for inference of genetic networks and regulatory modules, including a threshold gradient descent procedure for inference of Gaussian graphical models, a sparse regression mixture modeling approach for inference of regulatory modules, and the varying coefficient model for identifying regulatory subnetworks by integrating microarray time-course gene expression data and motif or ChIP-chip data. We present the statistical formulations of the problems, statistical methods, and results from analysis of real data sets. Areas of future research are also discussed.