Identification of biologically relevant subtypes via preweighted sparse clustering
Cluster analysis methods are used to identify homogeneous subgroups in a data set. Frequently one applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Conventional clustering methods often fail to identify such subgroups, particularly when there are a large number of high-variance features in the data set. Conventional methods may identify clusters associated with these high-variance features when one wishes to obtain secondary clusters that are more interesting biologically or more strongly associated with a particular outcome of interest. We describe a modification of the sparse clustering method of Witten and Tibshirani (2010) that can be used to identify such secondary clusters or clusters associated with an outcome of interest. We show that this method can correctly identify such clusters of interest in several simulation scenarios. The method is also applied to a large case-control study of TMD and a leukemia microarray data set.