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Model-based cluster analysis of microarray gene-expression data

Wei Pan1*, Jizhen Lin2 and Chap T Le1

Author Affiliations

1 Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455-0378, USA

2 Department of Otolaryngology, School of Medicine, University of Minnesota, 2001 6th Street SE, Minneapolis, MN 55455, USA

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Genome Biology 2002, 3:research0009-research0009.8  doi:10.1186/gb-2002-3-2-research0009

Published: 29 January 2002



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.


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.


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.