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Bioinformatics Advance Access published online on November 15, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti779
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received February 11, 2005
Revised November 10, 2005
Accepted November 11, 2005

Article

Classification of microarray data with factor mixture models

Francesca Martella 1 *

1 Dipartimento di Statistica, Probabilità e Statistiche Applicate, Universitá degli Studi di Roma "La Sapienza", P.le A. Moro, 5 - 00185, Rome (Italy)

* To whom correspondence should be addressed.
Francesca Martella, E-mail: francesca.martella{at}uniroma1.it


  Abstract

Motivation: The classification of few tissue samples on a very large number of genes represents a non standard problem in statistics but a usual one in microarray expression data analysis. In fact, the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. We consider high-density oligonucleotide microarray data, where the expression level is associated to an "absolute call", which represents a qualitative indication of whether or not a transcript is detected within a sample. The "absolute call" is generally not taken in consideration in analyses.

Results: In contrast to frequently used cluster analysis methods to analyze gene expression data, we consider a problem of classification of tissues and of the variables selection. We adopted methodologies formulated by Ghahramani and Hinton, 1996 and Rocci and Vichi, 2002 for simultaneous dimensional reduction of genes and classification of tissues; trying to identify genes (denominated "markers") that are able to distinguish between two known different classes of tissue samples. In this respect, we propose a generalization of the approach proposed by McLachlan et al., 2002 by advising to estimate the distribution of log LR statistic for testing one versus two component hypothesis in the mixture model for each gene considered individually, using a parametric boostrap approach. We compare conditional (on "absolute call") and unconditional analyses performed on data set described in Golub et al., 1999. We show that the proposed techniques improve the results of classification of tissue samples with respect to known results on the same benchmark data set.

Availability: The software of Ghahramani and Hinton, 1996 is written in Matlab and available in "Mixture of Factor Analyzers" on http://www.cs.toronto.edu/zoubin/ while the software of Rocci and Vichi, 2002 is available upon request from the authors.


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