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; X. Shirley Liu

doi:10.1038/ncomms11305

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, X. Shirley Liu

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32 Scopus citations

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.

Original language	English (US)
Article number	11305
Journal	Nature communications
Volume	7
DOIs	https://doi.org/10.1038/ncomms11305
State	Published - Apr 13 2016

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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10.1038/ncomms11305

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@article{9e75005c30ef4b2a96668595caa85150,

title = "High-dimensional genomic data bias correction and data integration using MANCIE",

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.",

author = "Chongzhi Zang and Tao Wang and Ke Deng and Bo Li and Sheng'en Hu and Qian Qin and Tengfei Xiao and Shihua Zhang and Meyer, {Clifford A.} and He, {Housheng Hansen} and Myles Brown and Liu, {Jun S.} and Yang Xie and Liu, {X. Shirley}",

note = "Funding Information: This work was partially supported by the US National Institutes of Health (NIH) grants U41HG7000 (X.S.L.), 1R01GM099409 (X.S.L.), 5R01CA172211 (Y.X.), 1R01CA152301 (Y.X.), Leukemia and Lymphoma Society (LLS) fellow award (C.Z.), and the National Natural Science Foundation of China (NSFC) grant 11401338 (K.D.) and 61422309 (S.Z.).",

year = "2016",

month = apr,

day = "13",

doi = "10.1038/ncomms11305",

language = "English (US)",

volume = "7",

journal = "Nature communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

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TY - JOUR

T1 - High-dimensional genomic data bias correction and data integration using MANCIE

AU - Zang, Chongzhi

AU - Wang, Tao

AU - Deng, Ke

AU - Li, Bo

AU - Hu, Sheng'en

AU - Qin, Qian

AU - Xiao, Tengfei

AU - Zhang, Shihua

AU - Meyer, Clifford A.

AU - He, Housheng Hansen

AU - Brown, Myles

AU - Liu, Jun S.

AU - Xie, Yang

AU - Liu, X. Shirley

N1 - Funding Information: This work was partially supported by the US National Institutes of Health (NIH) grants U41HG7000 (X.S.L.), 1R01GM099409 (X.S.L.), 5R01CA172211 (Y.X.), 1R01CA152301 (Y.X.), Leukemia and Lymphoma Society (LLS) fellow award (C.Z.), and the National Natural Science Foundation of China (NSFC) grant 11401338 (K.D.) and 61422309 (S.Z.).

PY - 2016/4/13

Y1 - 2016/4/13

N2 - 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.

AB - 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.

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JO - Nature communications

JF - Nature communications

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

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