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Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization
Zhang, Lihua1,2; Zhang, Shihua1,2,3
2019-07-26
Source PublicationNUCLEIC ACIDS RESEARCH
ISSN0305-1048
Volume47Issue:13Pages:6606-6617
AbstractHigh-throughput biological technologies (e.g. ChIP-seq, RNA-seq and single-cell RNA-seq) rapidly accelerate the accumulation of genome-wide omics data in diverse interrelated biological scenarios (e.g. cells, tissues and conditions). Integration and differential analysis are two common paradigms for exploring and analyzing such data. However, current integrative methods usually ignore the differential part, and typical differential analysis methods either fail to identify combinatorial patterns of difference or require matched dimensions of the data. Here, we propose a flexible framework CSMF to combine them into one paradigm to simultaneously reveal Common and Specific patterns via Matrix Factorization from data generated under interrelated biological scenarios. We demonstrate the effectiveness of CSMF with four representative applications including pairwise ChIP-seq data describing the chromatin modification map between K562 and Huvec cell lines; pairwise RNA-seq data representing the expression profiles of two different cancers; RNA-seq data of three breast cancer subtypes; and single-cell RNA-seq data of human embryonic stem cell differentiation at six time points. Extensive analysis yields novel insights into hidden combinatorial patterns in these multi-modal data. Results demonstrate that CSMF is a powerful tool to uncover common and specific patterns with significant biological implications from data of interrelated biological scenarios.
DOI10.1093/nar/gkz488
Language英语
Funding ProjectNational Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[61422309] ; National Natural Science Foundation of China[61379092] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; National Ten Thousand Talent Program for Young Top-notch Talents ; Key Research Program of the Chinese Academy of Sciences[KFZD-SW-219] ; National Key Research and Development Program of China[2017YFC0908405] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008]
WOS Research AreaBiochemistry & Molecular Biology
WOS SubjectBiochemistry & Molecular Biology
WOS IDWOS:000490556600010
PublisherOXFORD UNIV PRESS
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/35775
Collection应用数学研究所
Corresponding AuthorZhang, Shihua
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, CEMS,RCSDS, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
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Zhang, Lihua,Zhang, Shihua. Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization[J]. NUCLEIC ACIDS RESEARCH,2019,47(13):6606-6617.
APA Zhang, Lihua,&Zhang, Shihua.(2019).Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization.NUCLEIC ACIDS RESEARCH,47(13),6606-6617.
MLA Zhang, Lihua,et al."Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization".NUCLEIC ACIDS RESEARCH 47.13(2019):6606-6617.
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