ArticleGenomics, proteomics & bioinformatics2021
Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data.
Article in Genomics, proteomics & bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed.
- scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations.Journal of advanced research · 2026Article
- Single-Cell Omics Advances in Understanding Tissue Development and Complex Trait Formation in Sheep and Goats.Animals : an open access journal from MDPI · 2026Review
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- TGF-β mediates epigenetic control of innate antiviral responses and SIV reservoir size.Nature immunology · 2026Article
- STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation.bioRxiv : the preprint server for biology · 2026Article
- Transcriptome combined with single-cell data to construct a prognostic model for glycosylation-related genes in osteosarcoma.Discover oncology · 2026Article
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Rab37-mediated OPN secretion enriches SPP1Oncogenesis · 2026Article
- Adding highly variable genes to spatially variable genes can improve cell type clustering performance in spatial transcriptomics data.Bioinformatics advances · 2026Article
- Roll with the punches: Fibroblast growth factor 10 alleviates pyroptosis of alveolar epithelial cells in different immune niches.Clinical and translational medicine · 2026Article
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- Identification of common genes and biomarkers between Dermatomyositis and rheumatoid arthritis through integrated bioinformatics.PloS one · 2026Article
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Deciphering the interplay between inflammation and dysregulated autophagy in lupus nephritis through network analysis and experimental validation.Frontiers in cell and developmental biology · 2026Article
- Single-cell insights into tumor microenvironment heterogeneity and plasticity: transforming precision therapy in gastrointestinal cancers.Journal of experimental & clinical cancer research : CR · 2025Review
- ProjectSVR: mapping single-cell RNA-seq data to reference atlases by supported vector regression.Briefings in bioinformatics · 2025Article
- Single-cell multi-omics and spatial transcriptomics reveal the transcriptional regulatory landscape of clear cell renal cell carcinoma.Translational andrology and urology · 2025Article
- scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications.Briefings in bioinformatics · 2025Article
- A Novel Hypoxia-Immune Signature for Gastric Cancer Prognosis and Immunotherapy: Insights from Bulk and Single-Cell RNA-Seq.Current issues in molecular biology · 2025Article
- Integrated single-cell and bulk RNA sequencing analysis establishes a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in neuroblastoma.Discover oncology · 2025Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection (CP) and robust partial correlations (RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other, compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.
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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.