A mixture model-based approach to the clustering of microarray expression data

McLachlan, GJ, Bean, RW and Peel, D (2002) A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18 3: 413-422.

Document type:	Journal Article
Collections:	Excellence in Research Australia (ERA) - Collection School of Physical Sciences Publications

Author(s)	McLachlan, GJ Bean, RW Peel, D
Title	A mixture model-based approach to the clustering of microarray expression data
Journal name	Bioinformatics
Publication date	2002
Sub-type	Article
Volume number	18
Issue number	3
ISBN	1367-4803
ISSN	1367-4803
Start page	413
End page	422
Total pages	10
Editor(s)	C. Sander
Place of publication	United Kingdom
Publisher	Oxford University Press
Collection year	2002
Subject	C1 230204 Applied Statistics 780101 Mathematical sciences
Abstract	Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.
Keyword(s)	Mathematics, Interdisciplinary Applications Biochemical Research Methods Biotechnology & Applied Microbiology Computer Science, Interdisciplinary Applications Statistics & Probability Em Algorithm Patterns Classification Likelihood Mathematical & Computational Biology