  We propose a new method to visualize gene expression experiments inspired by
the latent semantic indexing, technique originally proposed in the textual
analysis context. By using the correspondence word-gene document-experiment, we
define an asymmetric similarity measure of association for genes that accounts
for potential hierarchies in the data, the key to obtain meaningful gene
mappings. We use the polar decomposition to obtain the sources of asymmetry of
the similarity matrix, which are later combined with previous knowledge.
Genetic classes of genes are identified by means of a mixture model applied in
the genes latent space. We describe the steps of the procedure and we show its
utility in the Human Cancer dataset.
