Identifying partitions of genes and tissue samples in microarray data.

An important challenge in microarray data analyses is the detection of genes which are differentially expressed across different types of experimental conditions. We provide an extension of a finite mixture model to the clustering of genes and experimental conditions, where the partition of experimental conditions may be known or unknown. In particular, the idea is to adopt a finite mixture approach with mean/covariance reparameterization, where an explicit distinction among upregulated genes, down-regulated genes, non-regulated genes (with respect to a reference) is made; moreover, within each of these groups; genes that are differentially expressed between two or more types of experimental conditions may be identified.