CyteType
Agentic, Evidence-Based Cell Type Annotation for Single-Cell RNA-seq
CyteType is an end-to-end cell type annotation system for single-cell RNA sequencing (scRNA-seq), designed for repeatable analysis pipelines rather than one-off prompting. It combines cluster-level marker genes, expression context, study metadata, literature retrieval, ontology mapping, and a dedicated review step in a structured workflow that operates directly on AnnData.
For Seurat workflows, use CyteTypeR.
[!IMPORTANT] Already installed CyteType? Open a terminal and run
pip install -U cytetypeto update to the latest version before you start.CyteType requires an API key. Use is free for academic and non-commercial research. Commercial use requires a license.
Quick Start
1. Install
pip install cytetype
2. Set up your API key
cytetype setup
This opens passwordless CyteType sign-in in your browser and saves the API key locally for automatic use from Python. You can also create or manage API keys in the dashboard.
Already have an API key? Save and validate it locally once:
cytetype login
3. Annotate with Scanpy
import scanpy as sc
from cytetype import CyteType
# Assumes preprocessed AnnData with clusters and marker genes
group_key = "clusters"
annotator = CyteType(
adata,
group_key=group_key,
rank_key=f"rank_genes_{group_key}",
n_top_genes=100,
)
adata = annotator.run(study_context="Human PBMC from a healthy donor")
sc.pl.umap(adata, color="cytetype_annotation_clusters")
What You Get
- Annotations: Cell type, subtype, and activation state for every cluster
- Cell Ontology mapping: Standardized CL IDs for comparison across studies
- Confidence and quality control: Confidence values, plus match scores against your existing labels
- Supporting evidence: Publications and condition-specific references behind each call
Example Report
Each analysis generates an HTML report with annotation decisions, reviewer comments, supporting evidence, and an embedded chat interface connected to your expression data.
Benchmarks
Across PBMC, bone marrow, tumor microenvironment, and cross-species datasets, the multi-agent approach outperforms existing annotation methods:
| Compared with | Improvement |
|---|---|
| GPTCellType | +388% |
| CellTypist | +268% |
| SingleR | +101% |
Methods and full results are in the preprint. You can also browse results on atlas-scale datasets.
Resources
- 🔐 CLI and Authentication: Set up API keys and manage saved credentials.
- ⚙️ Configuration: Customize annotation settings, LLM providers, and artifacts.
- 📋 Output Columns: Understand annotations and metadata added to AnnData.
- 🛠️ Troubleshooting: Resolve authentication, API, artifact, and LLM issues.
- 🧑💻 Development: Configure a local environment and contribute.
- 🎥 Introduction video: Watch a quick overview of CyteType.
- 💬 Discord community: Ask questions and get support.
Citation
Ahuja G, Antill A, Su Y, Dall'Olio GM, Basnayake S, Karlsson G, Dhapola P. Multi-agent AI enables evidence-based cell annotation in single-cell transcriptomics. bioRxiv 2025. doi: 10.1101/2025.11.06.686964
@article{cytetype2025,
title={Multi-agent AI enables evidence-based cell annotation in single-cell transcriptomics},
author={Gautam Ahuja, Alex Antill, Yi Su, Giovanni Marco Dall'Olio, Sukhitha Basnayake, Göran Karlsson, Parashar Dhapola},
journal={bioRxiv},
year={2025},
doi={10.1101/2025.11.06.686964},
url={https://www.biorxiv.org/content/10.1101/2025.11.06.686964v1}
}
License
CyteType is free for academic and non-commercial research under CC BY-NC-SA 4.0.
For commercial licensing, contact contact@nygen.io.
1 maintainer
Metadata
1 maintainer
Release files for cytetype 0.20.1
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