A mixture model-based approach to the clustering of microarray expression data.
Bioinformatics
; 18(3): 413-22, 2002 Mar.
Article
em En
| MEDLINE
| ID: mdl-11934740
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.AVAILABILITY:
EMMIX-GENE is available at http//www.maths.uq.edu.au/~gjm/emmix-gene/
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Leucemia Mieloide Aguda
/
Regulação Neoplásica da Expressão Gênica
/
Modelos Estatísticos
/
Neoplasias do Colo
/
Análise de Sequência com Séries de Oligonucleotídeos
/
Leucemia-Linfoma Linfoblástico de Células Precursoras
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2002
Tipo de documento:
Article
País de afiliação:
Austrália