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Genome Biol. 2004;5(12):R100. Epub 2004 Nov 30.

Estimating genomic coexpression networks using first-order conditional independence.

Author information

1
Department of Biology, University of Pennsylvania, 415 S University Avenue, Philadelphia, PA 19104, USA. paul.magwene@duke.edu

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.

PMID:
15575966
PMCID:
PMC545795
DOI:
10.1186/gb-2004-5-12-r100
[Indexed for MEDLINE]
Free PMC Article

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