ArticleiScience2021
CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- Dynamic uterine microenvironment drives endometrial adenocarcinoma carcinogenesis and progression.Communications biology · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- Improving atlas-scale single-cell annotation models with hierarchical cross-entropy loss.Nature computational science · 2026Article
- Estrogen signaling in PDGFRα+ cells positively regulates cortical bone metabolism via IGFBP5 in female mice.JBMR plus · 2026Article
- Biology-driven insights into the power of single-cell foundation models.Genome biology · 2025Article
- Automated descriptive cell type naming in flow and mass cytometry with CytoPheno.Scientific reports · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata.Briefings in bioinformatics · 2024Article
- PELI2 is a negative regulator of STING signaling that is dynamically repressed during viral infection.Molecular cell · 2024Article
- Uncertainty-aware single-cell annotation with a hierarchical reject option.Bioinformatics (Oxford, England) · 2024Article
- Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references.Cell reports methods · 2023Article
- Clinical evidence for a role of E2F1-induced replication stress in modulating tumor mutational burden and immune microenvironment.DNA repair · 2023Article
- Cell-type annotation with accurate unseen cell-type identification using multiple references.PLoS computational biology · 2023Article
- Genome-Wide Analysis of MYB Transcription Factors and Screening ofInternational journal of molecular sciences · 2023Article
- Screening the components of Saussurea involucrata for novel targets for the treatment of NSCLC using network pharmacology.BMC complementary medicine and therapies · 2022Article
- PlantGF: an analysis and annotation platform for plant gene families.Database : the journal of biological databases and curation · 2022Article
- scMRMA: single cell multiresolution marker-based annotation.Nucleic acids research · 2022Article
- Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases.Frontiers in cell and developmental biology · 2022Review
- Computational solutions for spatial transcriptomics.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.