ArticleBriefings in bioinformatics2024
A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- PromptSTG: prototype-guided prompting for few-shot spatial transcriptomics annotation.Briefings in bioinformatics · 2026Article
- Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients.Nature communications · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation.bioRxiv : the preprint server for biology · 2026Article
- Redefining cellular reprogramming with advanced genomic technologies.Nature reviews. Genetics · 2026Review
- CASSIA: a multi-agent large language model for automated and interpretable cell annotation.Nature communications · 2025Article
- GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data.bioRxiv : the preprint server for biology · 2025Article
- GTestimate: improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator.GigaScience · 2025Article
- Primer on machine learning applications in brain immunology.Frontiers in bioinformatics · 2025Review
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.
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Registered trials
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