CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
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Abstract
Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Archive. CellO's comprehensive training set enables it to run out of the box on diverse cell types and achieves competitive or even superior performance when compared to existing state-of-the-art methods. Lastly, CellO's linear models are easily interpreted, thereby enabling exploration of cell-type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO's models across the ontology.
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Hierarchies of cell communities inferred from single-cell RNA sequencing data are typically compared to the Cell Ontology to determine which communities correspond to known versus novel cell types.
“Likewise, hierarchies of cell communities inferred from single-cell RNA seq data are typically compared to the CL to determine which communities correspond to known versus novel cell types (Bernstein et al., 2021; Hou et al., 2019).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Bernstein et al., 2021
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Mapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75 · see citing works
- Authors
- Bernstein, M. N., Ma, Z., Gleicher, M., & Dewey, C. N.
- Affiliations
- Morgridge Institute for Research (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Dewey, Colin N.
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 24
- Issue
- 1
- Pages
- 101913
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC7753962
- OpenAlex
- W3113049731
- Cited by
- 45
- References
- 52
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 6 · 2023: 11 · 2022: 4 · 2021: 11 · 2020: 1
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Chan Zuckerberg Initiative
- National Institutes of Health
- National Science Foundation
- 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 classification, Cell Ontology, single-cell RNA sequencing, cell type annotation, cello, primary human cells
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