A hierarchical clustering and data fusion approach for disease subtype discovery

Pfeifer, B.; Schimek, M. G.

doi:10.1101/2020.01.16.909382

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bioRxiv · 10.1101/2020.01.16.909382

A hierarchical clustering and data fusion approach for disease subtype discovery

Pfeifer, B.·Schimek, M. G.

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Abstract

Recent advances in multi-omics clustering methods enable a more fine-tuned separation of cancer patients into clinical relevant clusters. These advancements have the potential to provide a deeper understanding of cancer progression and may facilitate the treatment of cancer patients. Here, we present a simple hierarchical clustering and data fusion approach, named HC-fused, for the detection of disease subtypes. Unlike other methods, the proposed approach naturally reports on the individual contribution of each single-omic to the data fusion process. We perform multi-view simulations with disjoint and disjunct cluster elements across the views to highlight fundamentally different data integration behaviour of various state-of-the-art methods. HC-fused combines the strengths of some recently published methods and shows superior performance on real world cancer data from the TCGA (The Cancer Genome Atlas) database. An R implementation of our method is available on GitHub (pievos101/HC-fused).

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Pfeifer, B., Schimek, M. G.. 2020-01-17. A hierarchical clustering and data fusion approach for disease subtype discovery. https://doi.org/10.1101/2020.01.16.909382

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