Parea: Multi-view ensemble clustering for cancer subtype discovery

• Published in: Journal of biomedical informatics 2023-07, Vol.143, p.104406-104406, Article 104406
• Main Authors: Pfeifer, Bastian, Bloice, Marcus D., Schimek, Michael G.
• Format: Article
• Language: English
• Subjects: Algorithms; Cluster Analysis; Disease subtyping; Hierarchical clustering; Humans; Machine Learning; Multi-omics; Multi-view clustering ensemble clustering; Neoplasms - genetics
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2023.104406

Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods have been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view patient data, comprising seven types of cancer. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms. [Display omitted] •Multi-omics clustering has the potential for the discovery of disease subtypes.•Parea is a flexible multi-view ensemble clustering method.•Our method outperforms the current state-of-the-art on real-world data sets.•Parea is made available as a Python software package: Pyrea.