ArticleGenome biology2019
A comparison of automatic cell identification methods for single-cell RNA sequencing data.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 338 papers, 1 of them a synthesis that pooled it.
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Who cites it
338 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Leveraging Single-Cell RNA Sequencing to Characterize Murine Ewing Sarcoma Xenografts.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Inferring Phenotypes of Single Cells Based on the Expression Profiles of Phenotype-Associated Marker Genes in Bulks and Single Cells.Interdisciplinary sciences, computational life sciences · 2026Article
- Single-cell Transcriptomics in Autoimmune Diseases: Mechanistic Insights, Cross-disease Convergence, and Translational Challenges in Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis.Clinical reviews in allergy & immunology · 2026Review
- Article
- Cell-level random splits leak group-owned answers in single-cell benchmarks.bioRxiv : the preprint server for biology · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-Scale Information and Metric Learning Framework for scDNAm Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.Briefings in bioinformatics · 2026Article
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- CellTypeAI: cell annotation for scRNA-seq using local generative-AI.Bioinformatics (Oxford, England) · 2026Article
- ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning.PLoS computational biology · 2026Article
- Article
- Integrating Mutation-Derived and Expression Features from Single-Cell RNA Sequencing: Pitfalls of Standard Cross-Validation in Small-Cohort Settings.International journal of molecular sciences · 2026Article
- Automatic extraction of mesenchymal stromal cells from single-cell RNA-sequencing data of human dental pulp.BioData mining · 2026Article
- scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations.Journal of advanced research · 2026Article
- A unified single-cell atlas of HNSCC: Toward characterizing HPV- and sex-associated TME variability.iScience · 2026Article
278 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification.
resultsHere, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments.
conclusionsWe present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.
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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.