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Multiple kernel learning for integrative consensus clustering of omic datasets.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Cabassi, Alessandra  ORCID logo  https://orcid.org/0000-0003-1605-652X
Kirk, Paul DW 

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 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. AVAILABILITY AND IMPLEMENTATION: R packages klic and coca are available on the Comprehensive R Archive Network. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Description

Keywords

Algorithms, Cluster Analysis, Consensus, Humans, Information Storage and Retrieval, Neoplasms

Journal Title

Bioinformatics

Conference Name

Journal ISSN

1367-4803
1367-4811

Volume Title

36

Publisher

Oxford University Press (OUP)

Rights

All rights reserved
Sponsorship
European Commission Horizon 2020 (H2020) Societal Challenges (847912)