  The discovery of disease subtypes is an essential step for developing
precision medicine, and disease subtyping via omics data has become a popular
approach. While promising, subtypes obtained from current approaches are not
necessarily associated with clinical outcomes. With the rich clinical data
along with the omics data in modern epidemiology cohorts, it is urgent to
develop an outcome-guided clustering algorithm to fully integrate the
phenotypic data with the high-dimensional omics data. Hence, we extended a
sparse K-means method to an outcome-guided sparse K-means (GuidedSparseKmeans)
method, which incorporated a phenotypic variable from the clinical dataset to
guide gene selections from the high-dimensional omics data. We demonstrated the
superior performance of the GuidedSparseKmeans by comparing with existing
clustering methods in simulations and applications of high-dimensional
transcriptomic data of breast cancer and Alzheimer's disease. Our algorithm has
been implemented into an R package, which is publicly available on GitHub
(https://github.com/LingsongMeng/GuidedSparseKmeans).
