Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.

Yi Qu; Shizhong Xu

doi:10.1093/bioinformatics/bth177

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PubMed · 15044244

Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.

Yi Qu·Shizhong Xu

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Abstract

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

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Algorithms Artificial Intelligence Cell Cycle Proteins Cluster Analysis Gene Expression Profiling Models, Genetic Models, Statistical Multivariate Analysis Normal Distribution Oligonucleotide Array Sequence Analysis Saccharomyces cerevisiae Proteins Sequence Alignment Sequence Analysis, DNA Software Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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Yi Qu, Shizhong Xu. 2004-03-25. Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.. https://doi.org/10.1093/bioinformatics%2Fbth177

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