The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data
Open accessAbstract
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.
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Cited by
Zhang, P., Zhang, D., Zhou, W., Wang, L., Wang, B., Zhang, T., & Li, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad518 · Cited by 675
Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., Panizzolo, E., Martowicz, A., Trebo, M., Pall, G., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.008 · Cited by 477
Ning, Z., Liu, Y., Wan, M., Zuo, Y., Chen, S., Shi, Z., Xu, Y., Li, H., Ko, H., Zhang, J., et al. (2024) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-024-00600-x · Cited by 20
Li, Z., Ma, L., Chen, M., Chen, X., Sha, M., & Hang, H. (2025) · JHEP Reports · doi:10.1016/j.jhepr.2024.101275 · Cited by 11
Metadata
- Authors
- Sun, D., Guan, X., Moran, A. E., Wu, L.-Y., Qian, D. Z., Schedin, P., Dai, M.-S., Danilov, A. V., Alumkal, J. J., Adey, A. C., Spellman, P. T., & Xia, Z.
- Venue
- Nature Biotechnology
- Published
- 2021-11-11
- Volume
- 40
- Issue
- 4
- Pages
- 527-538
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W3211405363
- Cited by
- 503
- References
- 60
- FWCI
- 18.85
- Type
- journal article
- Language
- en
- Open access
- green
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- Sun, D., Guan, X., Moran, A. E., Wu, L.-Y., Qian, D. Z., Schedin, P., Dai, M.-S., Danilov, A. V., Alumkal, J. J., Adey, A. C., Spellman, P. T., & Xia, Z. (2022). Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nature Biotechnology. https://doi.org/10.1038/s41587-021-01091-3
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