Integrative sparse $K$-means with overlapping group lasso in genomic applications for disease subtype discovery

Abstract

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse $K$-means (IS-$K$means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare IS-$K$means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.