Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery

Zhiguang Huo (), Li Zhu, Tianzhou Ma, Hongcheng Liu, Song Han, Daiqing Liao, Jinying Zhao and George Tseng ()
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Zhiguang Huo: University of Florida
Li Zhu: University of Pittsburgh
Tianzhou Ma: University of Maryland
Hongcheng Liu: University of Florida
Song Han: University of Florida
Daiqing Liao: University of Florida
Jinying Zhao: University of Florida
George Tseng: University of Pittsburgh

Statistics in Biosciences, 2020, vol. 12, issue 1, No 1, 22 pages

Abstract: Abstract Disease subtype discovery is an essential step in delivering personalized medicine. Disease subtyping via omics data has become a common approach for this purpose. With the advancement of technology and the lower price for generating omics data, multi-level and multi-cohort omics data are prevalent in the public domain, providing unprecedented opportunities to decrypt disease mechanisms. How to fully utilize multi-level/multi-cohort omics data and incorporate established biological knowledge toward disease subtyping remains a challenging problem. In this paper, we propose a meta-analytic integrative sparse Kmeans (MISKmeans) algorithm for integrating multi-cohort/multi-level omics data and prior biological knowledge. Compared with previous methods, MISKmeans shows better clustering accuracy and feature selection relevancy. An efficient R package, “MIS-Kmeans”, calling C++ is freely available on GitHub (https://github.com/Caleb-Huo/MIS-Kmeans).

Keywords: Two-way horizontal and vertical omics integration; Disease subtype discovery; ADMM; Prior group information (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12561-019-09242-6

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