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High-dimensional genomic data bias correction and data integration using MANCIE

Author

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  • Chongzhi Zang

    (Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
    Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute)

  • Tao Wang

    (Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center
    Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center)

  • Ke Deng

    (Center for Statistical Science, Tsinghua University)

  • Bo Li

    (Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
    Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute
    Harvard University)

  • Sheng’en Hu

    (School of Life Sciences, Tongji University)

  • Qian Qin

    (School of Life Sciences, Tongji University)

  • Tengfei Xiao

    (Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
    Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute and Harvard Medical School)

  • Shihua Zhang

    (National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

  • Clifford A. Meyer

    (Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
    Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute)

  • Housheng Hansen He

    (Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
    Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute and Harvard Medical School
    University of Toronto)

  • Myles Brown

    (Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute and Harvard Medical School)

  • Jun S. Liu

    (Harvard University)

  • Yang Xie

    (Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center
    Simons Comprehensive Cancer Center, University of Texas Southwestern Medical Center)

  • X. Shirley Liu

    (Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
    Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute)

Abstract

High-dimensional genomic data analysis is challenging due to noises and biases in high-throughput experiments. We present a computational method matrix analysis and normalization by concordant information enhancement (MANCIE) for bias correction and data integration of distinct genomic profiles on the same samples. MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust the data so as to achieve better consistency between sample-wise distances in the different profiles. MANCIE can improve tissue-specific clustering in ENCODE data, prognostic prediction in Molecular Taxonomy of Breast Cancer International Consortium and The Cancer Genome Atlas data, copy number and expression agreement in Cancer Cell Line Encyclopedia data, and has broad applications in cross-platform, high-dimensional data integration.

Suggested Citation

  • Chongzhi Zang & Tao Wang & Ke Deng & Bo Li & Sheng’en Hu & Qian Qin & Tengfei Xiao & Shihua Zhang & Clifford A. Meyer & Housheng Hansen He & Myles Brown & Jun S. Liu & Yang Xie & X. Shirley Liu, 2016. "High-dimensional genomic data bias correction and data integration using MANCIE," Nature Communications, Nature, vol. 7(1), pages 1-8, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11305
    DOI: 10.1038/ncomms11305
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    Cited by:

    1. Shengen Shawn Hu & Lin Liu & Qi Li & Wenjing Ma & Michael J. Guertin & Clifford A. Meyer & Ke Deng & Tingting Zhang & Chongzhi Zang, 2022. "Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Kaiwen Wang & Yuqiu Yang & Fangjiang Wu & Bing Song & Xinlei Wang & Tao Wang, 2023. "Comparative analysis of dimension reduction methods for cytometry by time-of-flight data," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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