Automatic cell type identification methods for single-cell RNA sequencing
Open accessAbstract
Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for scientists of many research disciplines due to its ability to elucidate the heterogeneous and complex cell-type compositions of different tissues and cell populations. Traditional cell-type identification methods for scRNA-seq data analysis are time-consuming and knowledge-dependent for manual annotation. By contrast, automatic cell-type identification methods may have the advantages of being fast, accurate, and more user friendly. Here, we discuss and evaluate thirty-two published automatic methods for scRNA-seq data analysis in terms of their prediction accuracy, F1-score, unlabeling rate and running time. We highlight the advantages and disadvantages of these methods and provide recommendations of method choice depending on the available information. The challenges and future applications of these automatic methods are further discussed. In addition, we provide a free scRNA-seq data analysis package encompassing the discussed automatic methods to help the easy usage of them in real-world applications.
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Many unsupervised scRNA-seq clustering methods have been proposed, which are followed by time-consuming and labor-costly annotations.
“Many unsupervised scRNA-seq clustering methods have been proposed2–4, which are followed by time-consuming and labor-costly annotations5.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [5]
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Kim, H., Lee, J., Kang, K., & Yoon, S. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.010 · Cited by 24 · see citing works
scPretrain: multi-task self-supervised learning for cell-type classification
Zhang, R., Luo, Y., Ma, J., Zhang, M., & Wang, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac007 · Cited by 20 · see citing works
Superscan: Supervised Single-Cell AnnotationPreprint
Shasha, C., Tian, Y., Mair, F., Miller, H. E. R., & Gottardo, R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.20.445014 · Cited by 5 · see citing works
scClassifR: Framework to accurately classify cell types in single-cell RNA-sequencing dataPreprint
Nguyen, V., & Griss, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.22.424025 · Cited by 2 · see citing works
MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experimentsPreprint
Kim, H., Lee, J., Kang, K., & Yoon, S. (2021) · Research Square · doi:10.21203/rs.3.rs-418249/v1 · Cited by 1 · see citing works
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Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · see citing works
- Authors
- Xie, B., Jiang, Q., Mora, A., & Li, X.
- Affiliations
- Sun Yat-sen University (CN)
- Nanjing Medical University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Jiang, Qin
- Mora, Antonio
- Li, Xuri
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2021-01
- Volume
- 19
- Pages
- 5874-5887
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC8572862
- OpenAlex
- W3208953781
- Cited by
- 66
- References
- 93
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 27 · 2023: 15 · 2022: 5 · 2021: 1
- FWCI
- 1.28
- 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)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (82070983)
- Sun Yat-sen University
- State Key Laboratory of Ophthalmology
- 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
- single-cell RNA sequencing, cell type annotation, unsupervised clustering, method comparison, prediction accuracy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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