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Estimating genomic coexpression networks using first-order conditional independence

Paul M Magwene12* and Junhyong Kim1

Author affiliations

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

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

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Citation and License

Genome Biology 2004, 5:R100  doi:10.1186/gb-2004-5-12-r100

Published: 30 November 2004


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.