Model-based cluster analysis of microarray gene-expression data.
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.
Pan
Wei
W
Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu
Lin
Jizhen
J
Le
Chap T
CT
D000818:Animals; D016000:Cluster Analysis; D004427:Ear Diseases / Q000235:genetics / Q000382:microbiology / Q000503:physiopathology; D004432:Ear, Middle / Q000382:microbiology / Q000503:physiopathology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011008:Pneumococcal Infections / Q000503:physiopathology; D051381:Rats; D017207:Rats, Sprague-Dawley; D013296:Streptococcus pneumoniae / Q000472:pathogenicity