COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms.

ABSTRACT: Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, incl

SUBMITTER: Rintala TJ 

PROVIDER: S-EPMC11326705 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature