Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization

doi:10.1093/nar/gkz488

CSpace > 应用数学研究所

	Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization
	Zhang, Lihua 1,2; Zhang, Shihua1,2,3
	2019-07-26
发表期刊	NUCLEIC ACIDS RESEARCH
ISSN	0305-1048
卷号	47 期号:13 页码:6606-6617
摘要	High-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.
DOI	10.1093/nar/gkz488
语种	英语
资助项目	National 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研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemistry & Molecular Biology
WOS记录号	WOS:000490556600010
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/35775
专题	应用数学研究所
通讯作者	Zhang, Shihua
作者单位	1.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
推荐引用方式 GB/T 7714	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.