Method

Estimating genomic coexpression networks using first-order conditional independence

Paul M Magwene^1,2 and Junhyong Kim¹

¹Department of Biology, University of Pennsylvania, 415 S University Avenue, Philadelphia, PA 19104, USA

²Current address: Department of Biology, Duke University, Durham, NC 27708, USA

author email corresponding author email

Genome Biology 2004, 5:R100doi:10.1186/gb-2004-5-12-r100

Published:	30 November 2004

Subject areas: Genome studies, Bioinformatics, Methods, Biochemistry and structural biology

Abstract

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.