Integrative hierarchical ensemble clustering for improved disease subtype discovery

Multi-omics clustering methods are used 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. Here, we propose a comprehensive framework for multi-omics hierarchical ensemble clustering. We provide a flexible environment that allows to build hierarchical clustering ensembles suitable for the available data and research goals. Survival analyses for data from The Cancer Genome Atlas (TCGA) indicate that our proposed ensembles provide more robust, and thus more reliable results than the state-of-the-art. We have implemented our architecture within the R-package HC-fused, which is freely available on Github.