{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zang C"],"funding":["NHGRI NIH HHS","NCI NIH HHS","NIGMS NIH HHS"],"pagination":["11305"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4833864"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7"],"pubmed_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."],"journal":["Nature communications"],"pubmed_title":["High-dimensional genomic data bias correction and data integration using MANCIE."],"pmcid":["PMC4833864"],"funding_grant_id":["5R01CA172211","U41 HG007000","R01 CA172211","1R01CA152301","R01 CA152301","U41HG7000","R01 GM099409","1R01GM099409"],"pubmed_authors":["Qin Q","Hu S","Xie Y","He HH","Deng K","Xiao T","Zhang S","Liu XS","Brown M","Wang T","Li B","Liu JS","Zang C","Meyer CA"],"additional_accession":[]},"is_claimable":false,"name":"High-dimensional genomic data bias correction and data integration using MANCIE.","description":"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.","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Apr","modification":"2025-04-25T17:37:46.5Z","creation":"2019-03-27T02:11:42Z"},"accession":"S-EPMC4833864","cross_references":{"pubmed":["27072482"],"doi":["10.1038/ncomms11305"]}}