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Bioinformatics Advance Access published online on June 9, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl284
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received February 16, 2006
Revised April 20, 2006
Accepted May 31, 2006

Article

Clustering microarray gene expression data using weighted Chinese restaurant process

Zhaohui Qin 1 *

1 Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029

* To whom correspondence should be addressed.
Zhaohui Qin, E-mail: qin{at}umich.edu


  Abstract

Motivation: Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters, and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging.

Results: We devised an improved model-based, Bayesian approach to cluster microarray gene expression data. Cluster assignment is carried out by an iterative weighted Chinese restaurant seating scheme such that the optimal number of clusters can be determined simultaneously with cluster assignment. The predictive updating technique was applied to improve the efficiency of the Gibbs sampler. An additional step is added during reassignment to allow genes that display complex correlation relationships such as time-shifted and/or inverted to be clustered together. Analysis done on a real dataset showed that as much as 30% of significant genes clustered in the same group display complex relationships with the consensus pattern of the cluster. Other notable features including automatic handling of missing data, quantitative measures of cluster strength and assignment confidence. Synthetic and real microarray gene expression datasets were analyzed to demonstrate its performance.

Availability: A computer program named Chinese restaurant cluster (CRC) has been developed based on this algorithm. The program can be downloaded at http://www.sph.umich.edu/csg/qin/CRC/.

Supplementary information: http://www.sph.umich.edu/csg/qin/CRC/.

Associate Editor: John Quackenbush
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