Classification of Microarray Data with Factor Mixture Models.

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 ‘abso- lute 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 and Rocci and Vichi for simul- taneous dimensional reduction of genes and classification of tissues; trying to identify genes (denominated ‘markers’) that are able to distin- guish between two known different classes of tissue samples. In this respect, we propose a generalization of the approach proposed by McLachlan et al. by advising to estimate the distribution of log LR statis- tic for testing one versus two component hypothesis in the mixture model for each gene considered individually, using a parametric bootstrap approach. We compare conditional (on ‘absolute call’) and unconditional analyses performed on dataset described in Golub et al. We show that the proposed techniques improve the results of classi- fication of tissue samples with respect to known results on the same benchmark dataset. Availability: The software of Ghahramani and Hinton is written in Matlab and available in ‘Mixture of Factor Analyzers’ on http://www. gatsby.ucl.ac.uk/zoubin/software.html while the software of Rocci and Vichi is available upon request from the authors