KMS Of Academy of mathematics and systems sciences, CAS
| Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data | |
Sun, Duanchen1,2; Guan, Xiangnan1,2; Moran, Amy E.3,4; Wu, Ling-Yun5 ; Qian, David Z.4; Schedin, Pepper3,4; Dai, Mu-Shui6; Danilov, Alexey, V7; Alumkal, Joshi J.8; Adey, Andrew C.4,6; Spellman, Paul T.1,4,6; Xia, Zheng1,2,4,9
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| 2021-11-11 | |
| Source Publication | NATURE BIOTECHNOLOGY
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| ISSN | 1087-0156 |
| Pages | 18 |
| Abstract | 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 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Biotechnology & Applied Microbiology |
| WOS Subject | Biotechnology & Applied Microbiology |
| WOS ID | WOS:000717445300003 |
| Publisher | NATURE PORTFOLIO |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/59559 |
| Collection | 应用数学研究所 |
| Corresponding Author | Xia, Zheng |
| Affiliation | 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 |
| Recommended Citation 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. |
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