ArticleNucleic acids research2021
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation.Journal of advanced research · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.Briefings in bioinformatics · 2026Article
- Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist.Briefings in bioinformatics · 2026Article
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 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
- scIMGCN: an Automatic Single-Cell Type Annotation Method Based on Interpretable Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Pan-cancer analysis of single-cell profiles with polygenic signals reveals genetic influences on tumor immunity across cancers.Communications biology · 2026Article
- GatorSC: multi-scale cell and gene graphs with mixture-of-experts fusion for single-cell transcriptomics.Briefings in bioinformatics · 2026Article
- scHilda: Hierarchical Integration of LLM with KG database for single cell type annotation.PLoS computational biology · 2026Article
- scSuperAnnotator: a platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data.Nucleic acids research · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrated single cell transcriptomics analysis for unraveling heterogeneity and plasticity of root cells for sustainable and regenerative agriculture.Frontiers in plant science · 2026Review
- GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- scKGBERT: a knowledge-enhanced foundation model for single-cell transcriptomics.Genome biology · 2025Article
- Chd4 remodels chromatin to control retinal cell type specification and lineage termination.Development (Cambridge, England) · 2025Article
- Large language models for drug discovery and development.Patterns (New York, N.Y.) · 2025Review
- Evaluation of cell type annotation reliability using a large language model-based identifier.Communications biology · 2025Article
- scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications.Briefings in bioinformatics · 2025Article
26 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN). Using human and mouse scRNA-seq data resources, we demonstrate the high performance and robustness of scDeepSort in labeling 764 741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed other known methods in annotating 76 external test datasets, reaching an 83.79% accuracy across 265 489 cells in humans and mice. Moreover, we demonstrate the universality of scDeepSort using more challenging datasets and using references from different scRNA-seq technology. Above all, scDeepSort is the first attempt to annotate cell types of scRNA-seq data with a pre-trained GNN model, which can realize the accurate cell-type annotation without additional references, i.e. markers or RNA-seq profiles.
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