-Unsupervised analysis: The aim of this analysis is to group samples on the basis of their molecular profi les without a priori knowledge of their phenotypic classifi cation. The fi rst step, which functions as a second detection fi lter, consists of selecting transcripts that are expressed in the dataset and display some degree of variability, which will facilitate sample clustering. For instance, this fi lter could select transcripts with expression levels that deviate by at least two-fold from the median intensity calculated across all samples. Importantly, this additional fi lter is applied independently of any knowledge of sample grouping or phenotype, which makes this type of analysis 'unsupervised' . Next, pattern discovery algorithms are often applied to identify 'molecular phenotypes' or trends in the data.