A common task in analysing microarray data is to determine which genes are differentially expressed under two (or more) kinds of tissue samples or samples submitted under different experimental conditions. It is well known that biological samples are heterogeneous due to factors such as molecular subtypes or genetic background, which are often unknown to the investigator. For instance, in experiments which involve molecular classification of tumours it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures.

Consequently, truly differentially expressed genes on sample subgroups may be lost if usual statistical approaches are used. In this work it is proposed a graphical tool which identifies genes with up and down regulation, as well as genes with differential expression which revels hidden subclasses, that are usually missed if current statistical methods are used.