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Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery |
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| Authors: | Huo Zhiguang Zhu Li Ma Tianzhou Liu Hongcheng Han Song Liao Daiqing Zhao Jinying Tseng George |
| Affiliation: | 1.Department of Biostatistics, University of Florida, Gainesville, USA ;2.Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA ;3.Department of Epidemiology and Biostatistics, University of Maryland, College Park, USA ;4.Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA ;5.Department of Surgery, University of Florida, Gainesville, USA ;6.Department of Anatomy and Cell Biology, University of Florida, Gainesville, USA ;7.Department of Epidemiology, University of Florida, Gainesville, USA ; |
| 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). |
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