ArticleNature communications2021
Leveraging the Cell Ontology to classify unseen cell types.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Single-cell chromatin accessibility and cis-regulatory element analyses in plants using the scPlantReg platform.Nature plants · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- snRNA sequencing-based skeletal muscle analysis of Jiangquan black pigs with different average daily growth rates.Scientific reports · 2026Article
- Improving atlas-scale single-cell annotation models with hierarchical cross-entropy loss.Nature computational science · 2026Article
- CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.Bioinformatics (Oxford, England) · 2026Article
- Biology-driven insights into the power of single-cell foundation models.Genome biology · 2025Article
- Ad hoc, post hoc and intrinsic-hoc in bioinformatics.Nature biotechnology · 2025Article
- Automated descriptive cell type naming in flow and mass cytometry with CytoPheno.Scientific reports · 2025Article
- HiCat: a semi-supervised approach for cell type annotation.Briefings in bioinformatics · 2025Article
- scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.Cell genomics · 2025Article
- Tumor innervation in cervical cancer: Prognostic insights from myelin-associated risk signatures.FASEB bioAdvances · 2025Article
- Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery.Nature communications · 2025Article
- scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type-specific transcriptome-wide association study framework.bioRxiv : the preprint server for biology · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Article
- Consensus prediction of cell type labels in single-cell data with popV.Nature genetics · 2024Article
- NF-κB inhibitor alpha controls SARS-CoV-2 infection in ACE2-overexpressing human airway organoids.Scientific reports · 2024Article
- Review
- DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation.Genome biology · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.
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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 Socratic graph.