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

  • Authors:

  • X. Liu;S. Sivaganesan;K. Y. Yeung;J. Guo;R. E. Bumgarner;Mario Medvedovic

  • Affiliations:

  • Department of Environmental Health, University of Cincinnati 3223 Eden Avenue ML 56, Cincinnati OH 45267, USA;Mathematical Sciences Department, University of Cincinnati Cincinnati, OH 45221, USA;Department of Microbiology, University of Washington Seattle, WA 98195, USA;Department of Environmental Health, University of Cincinnati 3223 Eden Avenue ML 56, Cincinnati OH 45267, USA;Department of Microbiology, University of Washington Seattle, WA 98195, USA;Department of Environmental Health, University of Cincinnati 3223 Eden Avenue ML 56, Cincinnati OH 45267, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

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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 Contact: Mario.Medvedovic@uc.edu Supplementary information: http://eh3.uc.edu/gimm/csimm