AWCluster: A Novel Integrative Network-Based Clustering of Multiomics for Subtype Analysis of Cancer Data.

ABSTRACT: The remarkable growth of multi-platform genomic profiles has led to the challenge of multiomics data integration. In this study, we present a novel network-based multiomics clustering founded on the Wasserstein distance from optimal mass transport. This distance has many important geometric properties making it a suitable choice for application in machine learning and clustering. Our proposed method of aggregating multiomics and Wasserstein distance clustering (aWCluster) is applied to breast carcinoma as well as bladder carcinoma, colorectal adenocarcinoma, renal carcinoma, lung non-small cell adenocarcinoma, and endometrial carcinoma from The Cancer Genome Atlas project. Subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylati

SUBMITTER: Pouryahya M 

PROVIDER: S-EPMC9518829 | biostudies-literature | 2022 May-Jun

REPOSITORIES: biostudies-literature