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Automated methods for cell type annotation on scRNA-seq data
Open accessAbstract
The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.
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“Annotation results obtained with pre-trained classifiers are strongly affected by the classifier type and the quality of the training data used to create the classifier54,55.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [55]
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Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098
Single-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 2,842 · cited 2×
“In tests using the Tabula Muris Atlas as a training dataset [47], ACTINN was highly accurate in classifying strictly related cellular subtypes, and was robust against batch effects arising from the use of different sequencing techniques.”
MethodMolecular 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 2,552
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
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Research works printed but not linked: 2
- The T.W. and Thoery C.: Past and present. J Anat Physiol 1890; 24: 253.
- 10.1038/s41592-019-0535-3 · DOI · Paper page
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Sorted by citations
Items 201–251 of 251
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Bickmann, L., Sandmann, S., Walter, C., & Varghese, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.09.579584
Nassiri, I., Fairfax, B. P., Lee, A., Wu, Y., Buck, D., & Piazza, P. (2023) · Figshare · doi:10.6084/m9.figshare.23639965
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Khozyainova, A. A., Valyaeva, A. A., Arbatsky, M. S., Isaev, S. V., Iamshchikov, P. S., Volchkov, E., Sabirov, M. S., Zainullina, V. R., & Chechekhin, V. I. (2023) · Biochemistry (Moscow) · doi:10.31857/s032097252302001x
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Smiljković, L., Mišić, M. J., Obradović, P., & Kovačević, V. (2024) · International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) · doi:10.1109/icetran62308.2024.10645128
Goričan, L., Gole, B., Jezernik, G., Krajnc, G., Potočnik, U., & Gorenjak, M. (2024) · Slovenian Veterinary Research · doi:10.26873/svr-1920-2024
Shang, J., Zhao, Y., Qin, B., Zhang, L., Meng, X., Ge, D., & Liu, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129708
scFTAT: a novel cell annotation method integrating FFT and transformer
Tang, B., & Chen, Y. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06061-z
Das, A., Das, G., Chakrabarti, A., & Ghosh, Z. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-9762-2_15
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Shi, G., Nagarajan, V., & Caspi, R. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.28.640907
Lin, X., Ren, J., Gao, L., Wei, Z., & Wang, J. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3553068
Jogani, S., Pol, A. S., Prajapati, M., Samal, A., Bhatia, K., Parmar, J., Patel, U., Shah, F., Vyas, N., & Gupta, S. K. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf243
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Shen, X., He, C., & Guan, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.23.671952
Guo, H., Cui, Q., Zhang, X., Chen, C., Zheng, W., Cai, C., Wang, X., & Wang, S. (2026) · Genome biology · doi:10.1186/s13059-026-04193-w
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Zheng, C., Wu, X., Zhu, H., Liu, X., Yang, A., He, R., Zhou, X., Wu, T., Xu, G., & Xie, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5643031
Fan, Y., Wang, W., & Zhang, Y. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108764
Jia, Q., Goodrich, J. A., & Conti, D. V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688299
Su, H., Zhang, C., Wang, Q., She, C. H., Chen, X., Dang, X., Yao, L. Y., Ni, K., Xiong, Z., Yin, D., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf650
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Menger, J., Krissmer, S. M., Kreutz, C., Binder, H., & Hackenberg, M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag338
Nassiri, I., Fairfax, B. P., Lee, A., Wu, Y., Buck, D., & Piazza, P. (2023) · Figshare · doi:10.6084/m9.figshare.23639962
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Maulding, N. D., Seninge, L., & Stuart, J. M. (2024) · Figshare · doi:10.6084/m9.figshare.26777389
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Balcı, M. A., Kuralay, S. C., Aksel, E. G., Shahpar, Z., Kayalar, Ö., & Eldem, V. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_38
Shin, E., Yun, S. G., & Cho, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.01.697324
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Yang, M., Qi, J., Lan, M., Huang, J., & Jin, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.31.702798
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Lee, Y., Wang, W., Starr, T. K., Noble-Orcutt, K. E., Myers, C. L., & Sachs, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.27.708585
Żyła, J., Mrukwa, A., Bilska, A. G., Szumała, K., Polańska, J., & Marczyk, M. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0053
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Rodosthenous, T., Shahrezaei, V., & Evangelou, M. (2026) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2026.112442
Liu, S., Wang, Z., Cao, H., Cui, J., He, Y., Li, Y., An, X., Zhang, L., & Song, Y. (2026) · Comprehensive physiology · doi:10.1002/cph4.70150
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Shin, J., Halberg, S., Liu, Y., Hazra, S., Lee, E. D.-I., & Roy, S. (2026) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120922-103729
Yang, S., Tyagi, S., Rosenmund, C., & Herman, M. A. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1819116
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Crouch, S. A. W., & Breitenbach, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-56492-8
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He, C., Filippidis, P., Xing, J., Kleinstein, S., & Guan, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.07.730754
Jia, Q., Goodrich, J. A., & Conti, D. V. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag395
Сергеевна, Ч. Е., Shcherbakova, L. N., Vigovskiy, M. A., Ильдарович, Т. А., Logunov, D. Y., Tychinin, D. I., Svetlichnyy, D. V., Kulebyakin, K. Y., Tkachuk, V., & Chechekhin, V. I. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag612
Sayed, U., Al-Hasnaawei, S., Mousa, H. M., Renuka Jyothi, S., Nayak, P. P., Janney-J, B., Singh, G., Singh‐Chauhan, A., & Kumar-Mishra, M. (2026) · Journal of Ovarian Research · doi:10.1186/s13048-026-02132-8
Okada, D., Takeda‐Kawaguchi, T., & Tezuka, K.-I. (2026) · BioData Mining · doi:10.1186/s13040-026-00581-x
Coding agents author interpretable single-cell embedding models from the literaturePreprint
Brunn, N., Krissmer, S. M., Frosch, M., Frick, M., Prinz, M., & Binder, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.07.737048
Jiang, S., Qi, C., Chen, Y., Song, X., & Wei, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.06.736807
Chen, X., Li, Y., Chang, R.-Y., Yan, T., Kong, F., Wang, H., & Wang, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3492-9_30
Islam, M. N., Suja, M. K. I. S., Takbir, A. A., & Kibria, M. K. (2026) · Computational and Systems Oncology · doi:10.1002/cso2.70025
Li, Y., You, Y., Chen, R., Wang, P., Zhang, Y., Zhang, L., & Deng, S. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1843602
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Wei, X., Ma, W., Wu, Z., & Wu, H. S. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5539-9_5
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İzci, E., & Doğan, B. (2026) · Research Square · doi:10.21203/rs.3.rs-10438566/v1
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Yao, Y., Shao, X., Chen, R., Qian, J., Gao, H., Peng, Y., Gao, J., & Fan, X. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag529
Nguyen, T.-H., Nguyen, T.-D., Hoang, O.-T., Can, T.-T. T., Nguyen, M.-H. T., Tran, M.-H., Chu, H.-C., & Nguyen, D.-V. (2026) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-026-09211-0
Liu, M. (2026) · Research Square · doi:10.21203/rs.3.rs-11109514/v1
Orsulik, D., & Niccoli, T. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1911064
Metadata
- Authors
- Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V.
- Affiliations
- University of Bonn (DE)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Center for Regenerative Therapies Dresden (DE)
- Corresponding author
- Busskamp, Volker
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2021-01
- Volume
- 19
- Pages
- 961-969
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC7873570
- OpenAlex
- W3124814274
- Cited by
- 249
- References
- 85
- Citations by year
- 2026: 45 · 2025: 62 · 2024: 45 · 2023: 48 · 2022: 35 · 2021: 14
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo
- Funders
- H2020 European Research Council (ERC-StG-678071 – ProNeurons)
- European Research Council
- Deutsche Forschungsgemeinschaft (EXC-2068-390729961, SPP2127)
- Volkswagen Foundation
- Technische Universität Dresden (EXC-2151-390873048)
- Rheinische Friedrich-Wilhelms-Universität Bonn
- 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 › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- cell type annotation, single-cell RNA sequencing, supervised classification, cellular heterogeneity
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