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Bioinformatics Advance Access published online on May 18, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl184
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received March 6, 2006
Revised April 18, 2006
Accepted May 8, 2006

Article

Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset

X. Liu 1, S. Sivaganesan 2, K. Y. Yeung 3, J. Guo 4, R. E. Bumgarner 3, and Mario Medvedovic 1 *

1 Department of Environmental Health, University of Cincinnati, 3223 Eden Av. ML 56, Cincinnati OH 45267; Division of Biomedical Informatics, Cincinnati Children's Hospital Research Foundation, Cincinnati, OH 45229
2 Mathematical Sciences Department, University of Cincinnati, Cincinnati, OH 45221
3 Department of Microbiology, University of Washington, Seattle, WA 98195
4 Department of Environmental Health, University of Cincinnati, 3223 Eden Av. ML 56, Cincinnati OH 45267

* To whom correspondence should be addressed.
Mario Medvedovic, E-mail: Mario.Medvedovic{at}uc.edu


  Abstract

Motivation: Identifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional "noise" introduced by non-informative measurements.

Results: We have developed a novel Bayesian hierarchical model and corresponding computational algorithms for clustering gene expression profiles across diverse experimental conditions and studies that accounts for context-specificity of gene expression patterns. The model is based on the Bayesian infinite mixtures framework and does not require a priori specification of the number of clusters. We demonstrate that explicit modeling of context-specificity results in increased accuracy of the cluster analysis by examining the specificity and sensitivity of clusters in microarray data. We also demonstrate that probabilities of co-expression derived from the posterior distribution of clusterings are valid estimates of statistical significance of created clusters.

Availability: The open-source package gimm is available at http://eh3.uc.edu/gimm.

Supplementary information: http://eh3.uc.edu/gimm/csimm.

Associate Editor: Golan Yona
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