CellReasoner: A reasoning-enhanced large language model for cell type annotation 🧬🧠

📄 Paper | 📊 Dataset | 🤖 Model

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📌 Table of Contents

• 📖 CellReasoner: A reasoning-enhanced large language model for cell type annotation 🧬🧠
  • 📌 Table of Contents
  • 🔬 Key Highlights
  • 🔑 Key Results
  • 🧠 Model Zoo
  • 🏋️‍♂️ Training
  • 📚 Training Data
  • 🚀 Usage
  • 📚 Citation

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🔬 Key Highlights

• Only a few expert-level reasoning samples are needed to activate reasoning in a 7B LLM.
• CellReasoner achieves expert-level interpretability and zero-/few-shot generalization.
• Demonstrated superior performance across various scRNA-seq and scATAC-seq datasets.
• Compatible with marker-by-marker annotation, ontology mapping, and biological reasoning.

🧠 Less data, more reasoning: CellReasoner achieves accurate, interpretable, and scalable cell annotation with minimal supervision.

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🔑 Key Results

PDAC dataset

Model	Score
Deepseek-V3	0.50
Deepseek-R1	0.53
ChatGPT-o3	0.58
ChatGPT-4o	0.63
singleR	0.68
CellReasoner-7B	0.73
CellReasoner-32B	0.74

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PBMC3K dataset

Model	Score
Deepseek-V3	0.52
Deepseek-R1	0.52
ChatGPT-4o	0.76
ChatGPT-o3	0.85
singleR	0.83
CellReasoner-7B	0.87
CellReasoner-32B	0.84

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🧠 Model Zoo

Our CellReasoner models are available on Hugging Face 🤗:

Model	Backbone	Link
CellReasoner-7B	Qwen2.5-7B-Instruct	🤗
CellReasoner-32B	QwQ-32B	🤗

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🏋️‍♂️ Training

We use the LLaMA-Factory framework for fine-tuning. It offers a flexible and efficient pipeline for supervised fine-tuning, LoRA, and multi-stage training strategies.

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📚 Training Data

We adopt a three-stage training strategy combining reasoning scaffold, biological knowledge infusion, and reasoning mode fusion.

Dataset Name	Training Stage	Samples
CellCoT	Reasoning Scaffold, Reasoning Mode Fusion	380
pancancer38k	Knowledge Infusion	37,187
pancancer4k	Internal test dataset	3,800

You can download the datasets from here.

🚀 Usage

🛠️ Step 1: Prepare Conda Environment

Make sure you have a working conda environment with the necessary dependencies installed. We recommend:

conda create -n cellreasoner python=3.11
conda activate cellreasoner
pip install -r requirements.txt

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🧪 Step 2: Preprocess Input Data

If your input is in Seurat .rds format, use the R preprocessing script:

Rscript s01.process_rds.R ./demo_data/pbmc_demo.rds ./output/ data/ranked_hvg.list

If your input is in AnnData .h5ad format, use the Python script:

python s01.process_h5ad.py \
--input_file ./demo_data/pbmc_demo.h5ad \
--output_path ./output_h5ad \
--ranked_hvg_list ./data/ranked_hvg.list

Both pipelines will generate the following output files:

output/
├── pbmc_demo.h5
└── pbmc_demo.meta.csv

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🧱 Step 3: Build Dataset for CellReasoner

Build the model input file using:

python s02.build_dataset.py \
--h5_path ./output/pbmc_demo.h5 \
--output_path ./output/ \
--meta_file_path ./output/pbmc_demo.meta.csv

If your metadata includes cell type labels (for scoring), specify the column name:

python s02.build_dataset.py \
--h5_path ./output/pbmc_demo.h5 \
--output_path ./output/ \
--meta_file_path ./output/pbmc_demo.meta.csv \
--cell_type_column "seurat_annotations"

This will generate:

output/
└── pbmc_demo_for_CellReasoner.json

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🤖 Step 4: Run Inference with CellReasoner

python s03.inference.py \
--model "CellReasoner-7B" \
--output_path "./output" \
--input_json "./output/pbmc_demo_for_CellReasoner.json" \
--batch_size 2

Result:

output/
└── pbmc_demo_CellReasoner_result.csv

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📊 Evaluation and Reasoning Visualization

To compute scores, generate plots, or view reasoning outputs, refer to:

s03.inference.ipynb

Citation

@article {Cao2025.05.20.655112,
author = {Cao, Guangshuo and Shen, Yi and Wu, Jianghong and Chao, Haoyu and Chen, Ming and Chen, Dijun},
title = {CellReasoner: A reasoning-enhanced large language model for cell type annotation},
elocation-id = {2025.05.20.655112},
year = {2025},
doi = {10.1101/2025.05.20.655112},
URL = {https://www.biorxiv.org/content/early/2025/05/26/2025.05.20.655112},
eprint = {https://www.biorxiv.org/content/early/2025/05/26/2025.05.20.655112.full.pdf},
journal = {bioRxiv}
}

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