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BMC Genomics. 2018 Mar 14;19(1):198. doi: 10.1186/s12864-018-4580-6.

Clustering multilayer omics data using MuNCut.

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Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06520, USA.
Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, 06520, USA.
Department of Statistics, Taiyuan University of Technology, 79 Yingze W St, Wanbailin Qu, Taiyuan Shi, Shanxi Sheng, 030024, People's Republic of China.



Omics profiling is now a routine component of biomedical studies. In the analysis of omics data, clustering is an essential step and serves multiple purposes including for example revealing the unknown functionalities of omics units, assisting dimension reduction in outcome model building, and others. In the most recent omics studies, a prominent trend is to conduct multilayer profiling, which collects multiple types of genetic, genomic, epigenetic and other measurements on the same subjects. In the literature, clustering methods tailored to multilayer omics data are still limited. Directly applying the existing clustering methods to multilayer omics data and clustering each layer first and then combing across layers are both "suboptimal" in that they do not accommodate the interconnections within layers and across layers in an informative way.


In this study, we develop the MuNCut (Multilayer NCut) clustering approach. It is tailored to multilayer omics data and sufficiently accounts for both across- and within-layer connections. It is based on the novel NCut technique and also takes advantages of regularized sparse estimation. It has an intuitive formulation and is computationally very feasible. To facilitate implementation, we develop the function muncut in the R package NcutYX.


Under a wide spectrum of simulation settings, it outperforms competitors. The analysis of TCGA (The Cancer Genome Atlas) data on breast cancer and cervical cancer shows that MuNCut generates biologically meaningful results which differ from those using the alternatives.


We propose a more effective clustering analysis of multiple omics data. It provides a new venue for jointly analyzing genetic, genomic, epigenetic and other measurements.


Clustering; Multilayer omics data; NCut

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