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Bioinformatics Advance Access published online on June 19, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp376
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Reconstruct Modular Phenotype-Specific Gene Networks by Knowledge-Driven Matrix Factorization

Xuerui Yang 1,2, Yang Zhou 3, Rong Jin 3 and Christina Chan 1,2,3,*

1Department of Chemical Engineering and Materials Science, 2Department of Biochemistry and Molecular Biology, 3Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA

*To whom correspondence should be addressed. Prof. Christina Chan, E-mail: krischan{at}egr.msu.edu


  Abstract

Motivation: Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e., the high computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the coregulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks.

Results: Considering the reconstruction of gene network as a matrix factorization problem, we first use the gene expression data to estimate a correlation matrix, and then factorize the correlation matrix to recover the gene modules and the interactions between them. Prior knowledge from Gene Ontology is integrated into the matrix factorization. We applied this KMF algorithm to hepatocellular carcinoma (HepG2) cells treated with free fatty acids (FFAs). By comparing the module networks for the different conditions, we identified the specific modules that are involved in conferring the cytotoxic phenotype induced by palmitate. Further analysis of the gene modules of the different conditions suggested individual genes that play important roles in palmitate-induced cytotoxicity. In summary, KMF can efficiently integrate gene expression data with prior knowledge, thereby providing a powerful method of reconstructing phenotype-specific gene networks and valuable insights into the mechanisms that govern the phenotype.

Contact: krischan{at}msu.edu

Associate Editor: Dr. Trey Ideker

Received on March 9, 2009; revised on May 19, 2009; accepted on June 9, 2009

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