Multiple kernel learning for integrative consensus clustering of genomic datasets

Diverse applications - particularly in tumour subtyping - have demonstrated the importance of integrative clustering as a means to combine information from multiple high-dimensional omics datasets. Cluster-Of-Clusters Analysis (COCA) is a popular integrative clustering method that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and the robustness of this approach to the inclusion of noisy datasets, or datasets that define conflicting clustering structures, is unclear. We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We show through extensive simulation studies that KLIC is more robust than COCA in a variety of situations. R code to run KLIC and COCA can be found at https://github.com/acabassi/klic