Parea: multi-view ensemble clustering for cancer subtype discovery
This work addresses the challenge of patient stratification in cancer research, offering an incremental improvement over existing methods.
The authors tackled the problem of cancer subtype discovery from multi-view data by developing Parea, a hierarchical ensemble clustering approach, which outperformed the state-of-the-art on six out of seven cancer types.
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.