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Model-based cluster analysis of microarray gene-expression data.

Wei Pan·Jizhen Lin·Chap T Le
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Abstract

BACKGROUND: Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic. RESULTS: The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels. CONCLUSIONS: Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.

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AnimalsCluster AnalysisEar DiseasesEar, MiddleGene Expression ProfilingModels, GeneticOligonucleotide Array Sequence AnalysisPneumococcal InfectionsRatsRats, Sprague-DawleyStreptococcus pneumoniaeJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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Wei Pan, Jizhen Lin, Chap T Le. 2002-01-29. Model-based cluster analysis of microarray gene-expression data.. https://doi.org/10.1186/gb-2002-3-2-research0009

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