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arXiv · 2209.15399

Parea: multi-view ensemble clustering for cancer subtype discovery

Bastian Pfeifer·Marcus D. Bloice·Michael G. Schimek
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Abstract

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 has 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 cancer patient data. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our developed 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.

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Bastian Pfeifer, Marcus D. Bloice, Michael G. Schimek. 2022-09-30. Parea: multi-view ensemble clustering for cancer subtype discovery. https://arxiv.org/abs/2209.15399

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