High-dimensional genomic data bias correction and data integration using MANCIE
He, Housheng Hansen
Liu, X. ShirleyNote: Order does not necessarily reflect citation order of authors.
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CitationZang, C., T. Wang, K. Deng, B. Li, S. Hu, Q. Qin, T. Xiao, et al. 2016. “High-dimensional genomic data bias correction and data integration using MANCIE.” Nature Communications 7 (1): 11305. doi:10.1038/ncomms11305. http://dx.doi.org/10.1038/ncomms11305.
AbstractHigh-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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:27320414