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

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 () and X. Shirley Liu ()
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Chongzhi Zang: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Tao Wang: Quantitative Biomedical Research Center, 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
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
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
Housheng Hansen He: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Myles Brown: Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute
Jun S. Liu: Harvard University
Yang Xie: Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center
X. Shirley Liu: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health

Nature Communications, 2016, vol. 7, issue 1, 1-8

Abstract: 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.

Date: 2016
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DOI: 10.1038/ncomms11305

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