Multiple kernel learning for integrative consensus clustering of omic datasets.

ABSTRACT:

Motivation

Diverse applications-particularly in tumour subtyping-have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear.

Results

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 contr

SUBMITTER: Cabassi A 

PROVIDER: S-EPMC7750932 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature