Bioinformatics Vol. 18 no. 9 2002
Pages 1194-1206
© 2002 Oxford University Press
Bayesian infinite mixture model based clustering of gene expression profiles
1 Center for Genome Information, Department of Environmental
Health, University of Cincinnati Medical Center, 3223 Eden Av. ML 56,
Cincinnati, OH~45267-0056, USA
2 Mathematical Sciences Department, University of Cincinnati,
4221 Fox Hollow Dr, Cincinnati, OH 45241, USA
Received on November 9, 2001
; revised on January 22, 2002 and March 4, 2002
; accepted on March 12, 2002
Motivation: The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data.
Results: We developed a clustering procedure based on the Bayesian infinite mixture model and applied it to clustering gene expression profiles. Clusters of genes with similar expression patterns are identified from the posterior distribution of clusterings defined implicitly by the stochastic data-generation model. The posterior distribution of clusterings is estimated by a Gibbs sampler. We summarized the posterior distribution of clusterings by calculating posterior pairwise probabilities of co-expression and used the complete linkage principle to create clusters. This approach has several advantages over usual clustering procedures. The analysis allows for incorporation of a reasonable probabilistic model for generating data. The method does not require specifying the number of clusters and resulting optimal clustering is obtained by averaging over models with all possible numbers of clusters. Expression profiles that are not similar to any other profile are automatically detected, the method incorporates experimental replicates, and it can be extended to accommodate missing data. This approach represents a qualitative shift in the model-based cluster analysis of expression data because it allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles. We also demonstrated the importance of incorporating the information on experimental variability into the clustering model.
Availability: The MS WindowsTM based program implementing the Gibbs sampler and supplemental material is available at http://homepages.uc.edu/~medvedm/BioinformaticsSupplement.htm
Contact: medvedm{at}email.uc.edu
* To whom correspondence should be addressed.
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