Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data

doi:10.1038/s41587-021-01091-3

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	Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data
	Sun, Duanchen 1,2; Guan, Xiangnan 1,2; Moran, Amy E.3,4; Wu, Ling-Yun5 ; Qian, David Z.4; Schedin, Pepper 3,4; Dai, Mu-Shui 6; Danilov, Alexey, V 7; Alumkal, Joshi J.8; Adey, Andrew C.4,6; Spellman, Paul T.1,4,6; Xia, Zheng 1,2,4,9
	2021-11-11
发表期刊	NATURE BIOTECHNOLOGY
ISSN	1087-0156
页码	18
摘要	Bulk and single cell measurements are integrated to identify phenotype-associated subpopulations of cells. Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.
DOI	10.1038/s41587-021-01091-3
收录类别	SCI
语种	英语
资助项目	NIH[5K01LM012877] ; NIH[1R21HL145426] ; NIH[1R01CA207377] ; NIH NIGMS[MIRA R35GM124704] ; Medical Research Foundation of Oregon ; NCI[R01 CA251245] ; NCI[P50 CA097186] ; NCI[P50 CA186786] ; NCI[P50 CA186786-07S1] ; NCI[R01CA244576] ; Department of Defense[W81XWH-16-1-0597]
WOS研究方向	Biotechnology & Applied Microbiology
WOS类目	Biotechnology & Applied Microbiology
WOS记录号	WOS:000717445300003
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59559
专题	应用数学研究所
通讯作者	Xia, Zheng
作者单位	1.Oregon Hlth & Sci Univ, Computat Biol Program, Portland, OR 97201 USA 2.Oregon Hlth & Sci Univ, Dept Biomed Engn, Portland, OR 97201 USA 3.Oregon Hlth & Sci Univ, Dept Cell Dev & Canc Biol, Portland, OR 97201 USA 4.Oregon Hlth & Sci Univ, Knight Canc Inst, Portland, OR 97201 USA 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 6.Oregon Hlth & Sci Univ, Dept Mol & Med Genet, Portland, OR 97201 USA 7.City Hope Natl Med Ctr, 1500 E Duarte Rd, Duarte, CA 91010 USA 8.Univ Michigan, Dept Internal Med, Rogel Canc Ctr, Ann Arbor, MI 48109 USA 9.Oregon Hlth & Sci Univ, Dept Mol Microbiol & Immunol, Portland, OR 97201 USA
推荐引用方式 GB/T 7714	Sun, Duanchen,Guan, Xiangnan,Moran, Amy E.,et al. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data[J]. NATURE BIOTECHNOLOGY,2021:18.
APA	Sun, Duanchen.,Guan, Xiangnan.,Moran, Amy E..,Wu, Ling-Yun.,Qian, David Z..,...&Xia, Zheng.(2021).Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data.NATURE BIOTECHNOLOGY,18.
MLA	Sun, Duanchen,et al."Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data".NATURE BIOTECHNOLOGY (2021):18.