Integrative clustering methods of multi-omics data for molecule-based cancer classifications

Abstract

One goal of precise oncology is to re-classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories: direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre-processing, post-clustering analysis and pathway-based analysis are also discussed.

Graphical abstract

Keywords

clustering / cancer classification / omics / integrative analysis

References

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ACKNOWLEDGEMENTS

This work is supported by National Basic Research Program of China (No. 2012CB316503), National Natural Science Foundation of China (Nos. 61370035 and 31361163004) and Tsinghua University Initiative Scientific Research Program.

The authors Dongfang Wang and Jin Gu declare that they have no conflict of interest.

This article does not contain any studies with human or animal subjects performed by any of the authors.

Funding

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg