ArticleBioinformatics (Oxford, England)2019
scMatch: a single-cell gene expression profile annotation tool using reference datasets.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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71 citing papers in PubMed.
- scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation.Journal of advanced research · 2026Article
- Age-Dependent Effects of Macrophage Derived Cathelicidin Antimicrobial Peptide on Bone Fracture Repair.bioRxiv : the preprint server for biology · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- scUmaper: An automated framework for doublet removal and cell-type annotation in single-cell transcriptomics.iScience · 2026Article
- CANTAO: guiding clustering and annotation in single-cell RNA sequencing using average overlap.Molecular systems biology · 2026Article
- SwarmMAP: swarm learning for decentralized cell type annotation in single cell sequencing data.NPJ systems biology and applications · 2026Article
- Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Adaptive individualized gene pair signatures distinguishing melanoma and predicting response to immune checkpoint blockade.iScience · 2025Article
- Network based simultaneous embedding of cells and marker genes from scRNA-seq studies.Briefings in bioinformatics · 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
- An overview of computational methods in single-cell transcriptomic cell type annotation.Briefings in bioinformatics · 2025Review
- ScInfeR: an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics.Briefings in bioinformatics · 2025Article
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data.BMC bioinformatics · 2025Article
- SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data.bioRxiv : the preprint server for biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- VICTOR: Validation and inspection of cell type annotation through optimal regression.Computational and structural biotechnology journal · 2024Article
- Modulation of SRC by SNTB1 activates the Hippo-YAP pathway during colon adenocarcinoma metastasis.Journal of translational medicine · 2024Article
11 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
motivationSingle-cell RNA sequencing (scRNA-seq) measures gene expression at the resolution of individual cells. Massively multiplexed single-cell profiling has enabled large-scale transcriptional analyses of thousands of cells in complex tissues. In most cases, the true identity of individual cells is unknown and needs to be inferred from the transcriptomic data. Existing methods typically cluster (group) cells based on similarities of their gene expression profiles and assign the same identity to all cells within each cluster using the averaged expression levels. However, scRNA-seq experiments typically produce low-coverage sequencing data for each cell, which hinders the clustering process.
resultsWe introduce scMatch, which directly annotates single cells by identifying their closest match in large reference datasets. We used this strategy to annotate various single-cell datasets and evaluated the impacts of sequencing depth, similarity metric and reference datasets. We found that scMatch can rapidly and robustly annotate single cells with comparable accuracy to another recent cell annotation tool (SingleR), but that it is quicker and can handle larger reference datasets. We demonstrate how scMatch can handle large customized reference gene expression profiles that combine data from multiple sources, thus empowering researchers to identify cell populations in any complex tissue with the desired precision. AVAILABILITY AND IMPLEMENTATION: scMatch (Python code) and the FANTOM5 reference dataset are freely available to the research community here https://github.com/forrest-lab/scMatch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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