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 cytetype to 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")

Try CyteType in Google Colab.

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.

View example report

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.