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Bioinformatics Advance Access published online on March 17, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp118
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identifying functional modules using expression profiles and confidence-scored protein interactions

Igor Ulitsky 1 and Ron Shamir 1,*

1Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel

*To whom correspondence should be addressed. Dr. Igor Ulitsky, E-mail: ulitskyi{at}tau.ac.il


  Abstract

Motivation: Microarray-based gene expression studies have great potential but are frequently difficult to interpret due to their overwhelming dimensions. Recent studies have shown that the analysis of expres-sion data can be improved by its integration with protein interaction networks, but the performance of these analyses has been hampered by the uneven quality of the interaction data.

Results: We present CEZANNE, a novel confidence-based method for extraction of functionally coherent co-expressed gene sets. CEZANNE uses probabilities for individual interactions, which can be computed by any available method. We propose a probabilistic model and a weighting scheme in which the likelihood of the con-nectivity of a subnetwork is related to the weight of its minimum cut. Applying CEZANNE to an expression dataset of DNA damage re-sponse in S. cerevisiae, we recover both known and novel modules and predict novel protein functions. We show that CEZANNE outper-forms previous methods for analysis of expression and interaction data.

Availability: CEZANNE is available as part of the MATISSE software: http://acgt.cs.tau.ac.il/matisse.

Associate Editor: Prof. Alfonso Valencia

Received on September 18, 2008; revised on January 21, 2009; accepted on February 26, 2009

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