# Multiple kernel learning for integrative consensus clustering of 'omic datasets

## TL;DR

This paper compares the existing COCA clustering method with a new multiple kernel learning approach called KLIC, demonstrating improved robustness and performance in integrative clustering of multi-omic datasets, especially with noisy or conflicting data.

## Contribution

The paper introduces KLIC, a novel multiple kernel learning-based method for integrative clustering, and systematically benchmarks it against COCA in various scenarios.

## Key findings

- KLIC effectively down-weights noisy datasets.
- KLIC outperforms COCA in simulated data scenarios.
- KLIC provides meaningful cancer subtyping results.

## Abstract

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, 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 compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. R packages "klic" and "coca" are available on the Comprehensive R Archive Network.