ArticleBMC bioinformatics2022
scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.Nature biotechnology · 2026Article
- Hierarchical classification of immune cell transcriptomes at population-scale.bioRxiv : the preprint server for biology · 2026Article
- Classification of indeterminate and off-target cell types within human kidney organoid differentiation.iScience · 2026Article
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- The IFNγ-CIITA-MHC II axis modulates melanoma cell susceptibility to NK-cell-mediated cytotoxicity.Molecular oncology · 2025Article
- Computational tracking of cell origins using CellSexID from single-cell transcriptomes.Cell reports methods · 2025Article
- scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.Bioinformatics (Oxford, England) · 2025Article
- Distinct immune cell infiltration patterns in pancreatic ductal adenocarcinoma (PDAC) exhibit divergent immune cell selection and immunosuppressive mechanisms.Nature communications · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Advancements in the Application of scRNA-Seq in Breast Research: A Review.International journal of molecular sciences · 2024Review
- A self-training interpretable cell type annotation framework using specific marker gene.Bioinformatics (Oxford, England) · 2024Article
- Single cell sequencing data identify distinct B cell and fibroblast populations in stricturing Crohn's disease.Journal of cellular and molecular medicine · 2024Article
- CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Article
- GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.Briefings in bioinformatics · 2023Article
- A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data.Communications biology · 2023Article
- Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing.Briefings in bioinformatics · 2023Article
- Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases.Frontiers in cell and developmental biology · 2022Review
- Automatic cell type identification methods for single-cell RNA sequencing.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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2 authors.
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Abstract
backgroundAutomatic cell type identification is essential to alleviate a key bottleneck in scRNA-seq data analysis. While most existing classification tools show good sensitivity and specificity, they often fail to adequately not-classify cells that are missing in the used reference. Additionally, many tools do not scale to the continuously increasing size of current scRNA-seq datasets. Therefore, additional tools are needed to solve these challenges.
resultsscAnnotatR is a novel R package that provides a complete framework to classify cells in scRNA-seq datasets using pre-trained classifiers. It supports both Seurat and Bioconductor's SingleCellExperiment and is thereby compatible with the vast majority of R-based analysis workflows. scAnnotatR uses hierarchically organised SVMs to distinguish a specific cell type versus all others. It shows comparable or even superior accuracy, sensitivity and specificity compared to existing tools while being able to not-classify unknown cell types. Moreover, scAnnotatR is the only of the best performing tools able to process datasets containing more than 600,000 cells.
conclusionsscAnnotatR is freely available on GitHub ( https://github.com/grisslab/scAnnotatR ) and through Bioconductor (from version 3.14). It is consistently among the best performing tools in terms of classification accuracy while scaling to the largest datasets.
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