ArticleHuman molecular genetics2018
Cell type discovery using single-cell transcriptomics: implications for ontological representation.
Article in Human molecular genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Advances in metabolomics of biomarkers for ischemic stroke: From bench to clinic.Neural regeneration research · 2026Article
- Genomic sequence evolution underlying human neocortical interareal diversification.Genome biology · 2026Article
- Review
- Single-cell Multiome Analysis of Chromatin State and Transcriptome in the Human Basal Ganglia.bioRxiv : the preprint server for biology · 2026Article
- A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography.bioRxiv : the preprint server for biology · 2025Article
- Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A temporal cortex cell atlas highlights gene expression dynamics during human brain maturation.Nature genetics · 2024Article
- Cell type-specific gene expression dynamics during human brain maturation.bioRxiv : the preprint server for biology · 2024Article
- Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.BMC methods · 2024Article
- Article
- Cochlear transcriptome analysis of an outbred mouse population (CFW).Frontiers in cellular neuroscience · 2023Article
- Single-cell sequencing: promises and challenges for human genetics.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2022Article
- Translational Organic Neural Interface Devices at Single Neuron Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Cell type matching in single-cell RNA-sequencing data using FR-Match.Scientific reports · 2022Article
- Machine learning for cell type classification from single nucleus RNA sequencing data.PloS one · 2022Article
- A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.Genome research · 2021Article
- Article
- FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test.Briefings in bioinformatics · 2021Article
- Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.Nature protocols · 2021Review
- Hierarchical progressive learning of cell identities in single-cell data.Nature communications · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Cells are fundamental function units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single-cell transcriptional profiling using RNA sequencing is producing 'big data', enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single-cell and single-nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method, based on random forest machine learning, for identifying sets of necessary and sufficient marker genes, which can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes being identified by high-throughput/high-content technologies findable, accessible, interoperable and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.
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Registered trials
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