Parea: multi-view ensemble clustering for cancer subtype discovery

TL;DR

Parea is a novel multi-view hierarchical ensemble clustering method designed for cancer subtype discovery, outperforming existing techniques on multiple cancer datasets and integrated into a flexible Python package.

Contribution

The paper introduces Parea, a new ensemble clustering approach that effectively combines multiple views for cancer subtype discovery, demonstrating superior performance over state-of-the-art methods.

Findings

Outperforms current methods on six of seven cancer types

Validated on real-world multi-view cancer data

Integrated into the Pyrea Python package for flexible workflows

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.