Awesome scRNA Cell Type Annotation

A comprehensive, curated collection of state-of-the-art single-cell RNA sequencing (scRNA-seq) cell type annotation tools, methods, databases, and resources for bioinformatics researchers and computational biologists.

Cell type annotation is a critical step in single-cell RNA sequencing analysis that involves assigning biological identities to clusters of cells based on their gene expression profiles. This repository serves as a central hub for high-quality tools and resources that can help researchers accurately identify cell types in their scRNA-seq datasets.

This collection only includes high-quality tools that either: - Have more than 100 stars on GitHub, OR - Are published in prestigious journals (Cell, Nature, Science and their sister journals, or Genome Biology)

Contents

• Cell Type Annotation Tools
  • Machine Learning Based
  • Reference Based
  • Marker Gene Based
  • Large Language Model Based
• Benchmark Studies
• Databases
  • Cell Marker Databases
  • Reference Datasets
• Tutorials and Workflows
• Community Resources
• Contributing
• Star History
• License

Cell Type Annotation Tools

| Tool | Description | Publication | GitHub Stars | Journal | |------|-------------|-------------|--------------|---------| | mLLMCelltype | An iterative multi-LLM consensus framework for accurate cell type annotation in single-cell RNA-seq data | Yang C, et al. (2025). Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data. | | bioRxiv | | GPTCelltype | Automatic cell type annotation with GPT-4 in single-cell RNA-seq analysis | Hou W, Ji Z. (2024). Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis. | | Nature Methods | | Seurat | R toolkit for single cell genomics | Satija R, et al. (2015). Spatial reconstruction of single-cell gene expression data. | | Nature Biotechnology | | SCANPY | Single-Cell Analysis in Python | Wolf FA, et al. (2018). SCANPY: large-scale single-cell gene expression data analysis. | | Genome Biology | | Celltypist | A tool for semi-automatic cell type classification based on logistic regression | Domínguez Conde C, et al. (2022). Cross-tissue immune cell analysis reveals tissue-specific features in humans. | | Science | | ScType | Fully-automated and ultra-fast cell-type identification using specific marker combinations | Ianevski A, et al. (2022). Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. | | Nature Communications | | scCATCH | Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data | Shao X, et al. (2020). scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data. | | iScience | | CellAssign | Automated, probabilistic assignment of cell types in scRNA-seq data | Zhang AW, et al. (2019). Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. | | Nature Methods | | scArches | Reference mapping for single-cell genomics with cell type transfer | Lotfollahi M, et al. (2021). Mapping single-cell data to reference atlases by transfer learning. | | Nature Biotechnology | | scGate | Marker-based purification of cell types from single-cell RNA-seq datasets | Andreatta M, et al. (2022). scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets. | | Bioinformatics | | popV | Ensemble method using popular vote of various cell-type transfer tools | Ergen C, Xing G, et al. (2024). Consensus prediction of cell type labels in single-cell data with popV. | | Nature Genetics | | Garnett | Cell type classification using marker genes and scRNA-seq data | Pliner HA, et al. (2019). Supervised classification enables rapid annotation of cell atlases. | | Nature Methods | | scmap | A tool for unsupervised projection of single cell RNA-seq data | Kiselev VY, et al. (2018). scmap: projection of single-cell RNA-seq data across data sets. | | Nature Methods | | SingleR | Reference-based single-cell RNA-seq annotation | Aran D, et al. (2019). Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. | | Nature Immunology | | Azimuth | Reference-based annotation for single-cell data | Hao Y, et al. (2021). Integrated analysis of multimodal single-cell data. | | Cell | | scBERT | Large-scale pretrained deep language model for cell type annotation | Yang F, et al. (2022). scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. | | Nature Machine Intelligence | | scGPT | Foundation model for single-cell multi-omics using generative AI | Cui H, Wang C, et al. (2024). scGPT: toward building a foundation model for single-cell multi-omics using generative AI. | | Nature Methods | | scDeepSort | Pre-trained cell-type annotation method using deep learning with a weighted graph neural network | Shao X, et al. (2021). scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. | | Nucleic Acids Research | | scJoint | Transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq data | Lin Y, et al. (2022). scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning. | | Nature Biotechnology |

Benchmark Studies

| Study | Description | Publication | Journal | |-------|-------------|-------------|---------| | Single-cell RNA-seq annotation benchmarking | Systematic comparison of single-cell RNA-seq cell type annotation methods | Abdelaal et al., 2019 | Nature Methods | | A comparison of automatic cell identification methods | Benchmarking automatic cell identification methods for scRNA-seq | Zhao et al., 2020 | Genome Biology |

Databases

| Database | Description | Publication | URL | |----------|-------------|-------------|-----| | CellMarker | Database of cell markers in different tissues | Zhang et al., 2019 | Link | | PanglaoDB | Single-cell RNA sequencing database for expression data | Franzén et al., 2019 | Link | | Cell Ontology | Structured vocabulary for cell types | Diehl et al., 2016 | Link |

Tutorials and Workflows

| Title | Description | URL | |-------|-------------|-----| | Orchestrating Single-Cell Analysis with Bioconductor | Comprehensive guide to scRNA-seq analysis | Link | | Seurat - Guided Clustering Tutorial | Tutorial for cell type identification with Seurat | Link |

Community Resources

| Resource | Description | URL | |----------|-------------|-----| | Single Cell Genomics Day | Annual event dedicated to single-cell genomics | Link | | Single Cell Omics | Reddit community for single-cell omics discussions | Link | | Bioconductor Single Cell | Collection of Bioconductor packages for single-cell analysis | Link |

Contributing

We welcome contributions to this repository! Please read our contributing guidelines before submitting a pull request.

If you find this resource useful, please consider giving it a ⭐️ star to help others discover it!

Star History

License

This repository is licensed under the MIT License - see the LICENSE file for details.

Keywords: single-cell RNA sequencing, scRNA-seq, cell type annotation, bioinformatics, computational biology, marker genes, reference mapping, machine learning, large language models, cell atlas, cell ontology