Identification of relevant subtypes via preweighted sparse clustering

TL;DR

This paper introduces a modified sparse clustering method designed to identify biologically relevant subgroups associated with specific outcomes, especially in high-dimensional biomedical data, improving upon conventional clustering techniques.

Contribution

The paper proposes a preweighted sparse clustering approach that effectively detects outcome-related subgroups in high-variance feature data, addressing limitations of traditional methods.

Findings

Successfully identified outcome-associated subgroups in simulations

Applied method to temporomandibular disorder cohort data

Analyzed leukemia microarray dataset with promising results

Abstract

Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently 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 generally do not 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. A modification of sparse clustering can be used to identify such secondary clusters or clusters associated with an outcome of interest. This method correctly identifies such clusters of interest in several simulation scenarios. The method is also applied to a large prospective cohort study of temporomandibular disorders and a leukemia microarray data set.