Supervised classification enables rapid annotation of cell atlases
Open accessAbstract
Single cell technologies for profiling tissues or even entire organisms are rapidly being adopted. However, the manual process by which cell types are typically annotated in the resulting data is labor-intensive and increasingly rate-limiting for the field. Here we describe Garnett, an algorithm and accompanying software for rapidly annotating cell types in scRNA-seq and scATAC-seq datasets, based on an interpretable, hierarchical markup language of cell type-specific genes. Garnett successfully classifies cell types in tissue and whole organism datasets, as well as across species.
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Tools such as scmap or Garnett can transfer annotations between a reference and a dataset by directly comparing gene expression profiles of annotated reference clusters to individual cells.
“By directly comparing the gene expression profiles of annotated reference clusters to individual cells, tools such as scmap (Kiselev et al, 2018b) or Garnett (preprint: Pliner et al, 2019) can transfer annotations between the reference and the dataset.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Pliner et al, 2019
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Cites
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
A statistical interpretation of term specificity and its application in retrieval
Jones, K. S. (2021) · J. Documentation · doi:10.1108/eb026526 · Cited by 5,541 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Self-citationSingle-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303 · see citing works
Phenotype molding of stromal cells in the lung tumor microenvironment
Lambrechts, D., Wauters, E., Boeckx, B., Aibar, S., Nittner, D., Burton, O., Bassez, A., Decaluwé, H., Pircher, A., Van den Eynde, K., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0096-5 · Cited by 1,648 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
CellMarker: a manually curated resource of cell markers in human and mouse
Zhang, X., Lan, Y., Xu, J., Quan, F., Zhao, E., Deng, C., Luo, T., Xu, L., Liao, G., Yan, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky900 · Cited by 1,524 · see citing works
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Self-citationLun, A. T. L., Riesenfeld, S. J., Andrews, T. S., Dao, T. P., Gomes, T., Jamboree, P. I. T. 1. H. C. A., & Marioni, J. C. (2019) · Genome biology · doi:10.1186/s13059-019-1662-y · Cited by 1,042 · see citing works
Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues
Merkin, J., Russell, C., Chen, P., & Burge, C. B. (2012) · Science · doi:10.1126/science.1228186 · Cited by 972 · see citing works
Exponential scaling of single-cell RNA-seq in the past decade
Svensson, V., Vento‐Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 936 · see citing works
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Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789 · see citing works
Self-citationA Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759 · see citing works
Self-citationBard, J., Rhee, S. Y., & Ashburner, M. (2005) · Genome biology · doi:10.1186/gb-2005-6-2-r21 · Cited by 416 · see citing works
glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
Friedman, J. H., Hastie, T. J., Tibshirani, R. J., Narasimhan, B., Tay, J. K., Simon, N., Yang, J., & Taylor, J. (2008) · doi:10.32614/cran.package.glmnet · Cited by 387 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.012 · Cited by 384 · see citing works
The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability
Diehl, A. D., Meehan, T. F., Bradford, Y. M., Brush, M., Dahdul, W., Dougall, D. S., He, Y. O., Osumi-Sutherland, D., Ruttenberg, A., Sarntivijai, S., et al. (2016) · Journal of Biomedical Semantics · doi:10.1186/s13326-016-0088-7 · Cited by 332 · see citing works
Detection and removal of barcode swapping in single-cell RNA-seq data
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L., & Marioni, J. C. (2018) · Nature Communications · doi:10.1038/s41467-018-05083-x · Cited by 324 · see citing works
Exponential scaling of single-cell RNA-seq in the last decadePreprint
Svensson, V., Tormo, R. V., & Teichmann, S. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01379 · Cited by 166 · see citing works
Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing dataPreprint
Lun, A., Riesenfeld, S. J., Andrews, T., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/234872 · Cited by 104 · see citing works
A web server for comparative analysis of single-cell RNA-seq data
Alavi, A., Ruffalo, M., Parvangada, A., Huang, Z., & Bar‐Joseph, Z. (2018) · Nature Communications · doi:10.1038/s41467-018-07165-2 · Cited by 59 · see citing works
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- Authors
- Pliner, H. A., Shendure, J., & Trapnell, C.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Shendure, Jay
- Trapnell, Cole
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2019-02-04
- Volume
- 16
- Issue
- 10
- Pages
- 983-986
- ISSN
- 1548-7091 (ISSN-L)
- PMC
- PMC6791524
- OpenAlex
- W2971398276
- Cited by
- 498
- References
- 27
- Citations by year
- 2026: 45 · 2025: 58 · 2024: 76 · 2023: 73 · 2022: 66 · 2021: 88 · 2020: 73 · 2019: 18 · 2014: 1
- FWCI
- 21.27
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- Funders
- National Science Foundation (1256082)
- Howard Hughes Medical Institute
- U.S. Department of Health and Human Services (RC2 DK114777, R01HL118342, R01HG006283, DP1HG007811, DP2HD088158, U54DK107979)
- Alfred P. Sloan Foundation
- Paul G. Allen Frontiers Group
- National Institutes of Health (DGE-1256082)
- National Heart, Lung, and Blood Institute (R01HL118342, U54HL145611)
- National Human Genome Research Institute (DP1HG007811, R01HG006283, R01HG010632)
- National Institute of Diabetes and Digestive and Kidney Diseases (RC2DK114777, U54DK107979)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD088158)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- cell type annotation, supervised classification, single-cell transcriptomics, single-cell chromatin accessibility, cell type-specific genes, marker genes, cell atlases
- MeSH terms
- Algorithms, Animals, Atlases as Topic, Humans, Transcription, Genetic, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Datasets as Topic
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