ConsensusClusterPlus (Tutorial)

Consensus Clustering [1] is a method that provides quantitative evidence for determining the number and membership of possible clusters within a dataset, such as microarray gene expression. This method has gained popularity in cancer genomics, where new molecular subclasses of disease have been discovered [3, 4]. The Consensus Clustering method involves subsampling from a set of items, such as microarrays, and determines clusterings of specified cluster counts (k). Then, pairwise consensus values, the proportion that two items occupied the same cluster out of the number of times they occurred in the same subsample, are calculated and stored in a symmetrical consensus matrix for each k. The consensus matrix is summarized in several graphical displays that enable a user to decide upon a reasonable cluster number and membership. A web-based version of Consensus Clustering is publicly available [5]. For a formal description, see [1].