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Background Cluster analysis is widely used in cancer research to discover molecular subgroups that inform subsequent laboratory investigations and define risk classification criteria for subsequent clinical trials. However, for any data set, there are a very large number of candidate cluster analysis methods (CCAMs) due to the many choices for feature selection criteria, number of selected features, number of clusters to define, etc. Frequently, a specific CCAM is chosen without quantifying the validity of its results in terms of reproducibility or distinctiveness of the reported subgroups.

bmc bioinformatics

Dunn Index Bootstrap (DIBS): A procedure to empirically select a cluster analysis method that identifies biologically and clinically relevant molecular disease subgroups