Bioinformatics Advance Access originally published online on April 29, 2004
Bioinformatics 2004 20(16):2562-2571; doi:10.1093/bioinformatics/bth285
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Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments
1 INSERM U472, 16 Avenue Paul Vaillant Couturier, 94807 Villejuif Cedex, France, 2 Department of Epidemiology and Public Health, Imperial College, Norfolk Place, London W2 1PG, UK and 3 Institut Curie, 26 rue d'Ulm, 75248 Paris Cedex, France
Received on February 3, 2004; revised on March 22, 2004; accepted on April 13, 2004
Advance Access Publication April 29, 2004
Motivation: Multiclass response (MCR) experiments are those in which there are more than two classes to be compared. In these experiments, though the null hypothesis is simple, there are typically many patterns of gene expression changes across the different classes that led to complex alternatives. In this paper, we propose a new strategy for selecting genes in MCR that is based on a flexible mixture model for the marginal distribution of a modified F-statistic. Using this model, false positive and negative discovery rates can be estimated and combined to produce a rule for selecting a subset of genes. Moreover, the method proposed allows calculation of these rates for any predefined subset of genes.
Results: We illustrate the performance our approach using simulated datasets and a real breast cancer microarray dataset. In this latter study, we investigate predefined subset of genes and point out interesting differences between three distinct biological pathways.
Availability: http://www.bgx.org.uk/software.html
Contact: broet{at}vjf.inserm.fr
* To whom correspondence should be addressed.
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