Classification of microarray data with factor mixture models

摘要

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.