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 training data.
“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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- 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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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