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

Author

Listed:
  • 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)

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).

Suggested Citation

  • Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:1:d:10.1007_s12561-019-09242-6
    DOI: 10.1007/s12561-019-09242-6
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    References listed on IDEAS

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