MetaClustering: discovery of the different sample clusterings in gene expression data.

Clustering of the samples is a standard procedure for the analysis of gene expression data, for instance to discover cancer subtypes. However, more than one biologically meaningful clustering can exist, depending on the genes chosen. We propose here to group the genes in function of the clustering of the samples they fit. This allows to determine directly the different clusterings of the samples present in the data. As a clustering is a structure, genes belonging to the same group are functions of the same structure. Hence, the determination of groups of genes which support the same clustering could also be viewed as the detection of non-linearly linked genes. MetaClustering was applied successfully to simulated data. It also recovered the known clustering of real cancer data, which was impossible using the complete set of genes. Finally, it clustered together cell-cycle genes, showing its ability to group genes related in a non-linear way.