Bioinformatics Advance Access originally published online on March 25, 2004
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Bioinformatics 20(12) © Oxford University Press 2004; all rights reserved.
Supervised cluster analysis for microarray data based on multivariate Gaussian mixture
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Received on May 25, 2003; revised on November 26, 2003; accepted on January 29, 2004
Advance Access Publication March 25, 2004
Motivation: Grouping genes having similar expression patterns is called gene clustering, which has been proved to be a useful tool for extracting underlying biological information of gene expression data. Many clustering procedures have shown success in microarray gene clustering; most of them belong to the family of heuristic clustering algorithms. Model-based algorithms are alternative clustering algorithms, which are based on the assumption that the whole set of microarray data is a finite mixture of a certain type of distributions with different parameters. Application of the model-based algorithms to unsupervised clustering has been reported. Here, for the first time, we demonstrated the use of the model-based algorithm in supervised clustering of microarray data.
Results: We applied the proposed methods to real gene expression data and simulated data. We showed that the supervised model-based algorithm is superior over the unsupervised method and the support vector machines (SVM) method.
Availability: The program written in the SAS language implementing methods I–III in this report is available upon request. The software of SVMs is available in the website http://svm.sdsc.edu/cgi-bin/nph-SVMsubmit.cgi
Contact: xu{at}genetics.ucr.edu
* To whom correspondence should be addressed.
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