  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 existing 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. An unified objective function was proposed, which was comprised of (i)
weighted K-means to perform sample clusterings; (ii) lasso regularizations to
perform gene selection from the high-dimensional omics data; (iii)
incorporation of a phenotypic variable from the clinical dataset to facilitate
biologically meaningful clustering results. By iteratively optimizing the
objective function, we will simultaneously obtain a phenotype-related sample
clustering results and gene selection results. 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).
