CyteType

Agentic, Evidence-Based Cell Type Annotation for Single-Cell RNA-seq

CyteType performs automated cell type annotation in single-cell RNA sequencing (scRNA-seq) data. It uses a multi-agent AI architecture to deliver transparent, evidence-based annotations with Cell Ontology mapping.

Integrates with Scanpy and Seurat workflows.

Preprint published: Nov. 7, 2025: bioRxiv link - Dive into benchmarking results

Why CyteType?

Cell type annotation is one of the most time-consuming steps in single-cell analysis. It typically requires weeks of expert curation, and the results often vary between annotators. When annotations do get done, the reasoning is rarely documented; this makes it difficult to reproduce or audit later.

CyteType addresses this with a novel agentic architecture: specialized AI agents collaborate on marker gene analysis, literature evidence retrieval, and ontology mapping. The result is consistent, reproducible annotations with a full evidence trail for every decision.

Key Features

Also included: interactive HTML reports, Scanpy/Seurat compatibility (R wrapper via CyteTypeR), and no API keys required out of the box.

📹 Watch CyteType intro video

Quick Start

Installation

pip install cytetype

Basic Usage 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='rank_genes_' + group_key, 
    n_top_genes=100
)
adata = annotator.run(study_context="Human PBMC from healthy donor")
sc.pl.umap(adata, color='cytetype_annotation_clusters')

🚀 Try it in Google Colab

Note: No API keys required for default configuration. See custom LLM configuration for advanced options.

Using R/Seurat? → CyteTypeR

Documentation

Output Reports

Each analysis generates an HTML report documenting annotation decisions, reviewer comments and an embedded chat interface for further exploration.

View example report

Benchmarks

Validated across PBMC, bone marrow, tumor microenvironment, and cross-species datasets. CyteType's agentic architecture consistently outperforms existing annotation methods:

Browse CyteType results on atlas scale datasets

Citation

If you use CyteType in your research, please cite our preprint:

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 [email protected].