Multi-omics clustering approaches have great potential for the discovery of disease subtypes.
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Results of most existing multi-omics clustering methods are difficult to interpret.
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HC-fused provides a flexible, versatile, and extendable framework for multi-omics clustering.
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For synthetic as well as real-world TCGA cancer data HC-fused shows superior results compared to state-of-the-art approaches.
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 behavior 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).
