data/README.md

CellVerse: Do Large Language Models Really Understand Cell Biology?

Official repository for the paper "CellVerse: Do Large Language Models Really Understand Cell Biology?"

🌟 For more details, please refer to the project page: https://cellverse-cuhk.github.io.

[🌐 Webpage] [📖 Paper] [🤗 Huggingface Dataset] [🏆 Leaderboard]

💥 News

• [2025.05.08] 🚀 We release the test set of CellVerse at [🤗 Huggingface Dataset], and the inference code!

👀 About CellVerse

The capabilities of Large Language Models (LLMs) in Cell Biology Understanding remain insufficiently evaluated and understood. We investigate the differences between the traditional single-cell analysis paradigm and the language-centric paradigm, and find that the latter offers advantages in terms of unification, user-friendliness, and interpretability.

To this end, we introduce CellVerse, a language-centric single-cell analysis benchmark designed for an equitable and in-depth evaluation of LLMs. The whole dataset encompasses four types of single-cell multi-omics data (scRNA-seq, CITE-seq, ASAP-seq, and ASAP-seq data) and spans three sub-tasks: cell type annotation, drug response prediction, and perturbation analysis. This approach allows CellVerse to uncover both capabilities and limitations of current LLMs in understanding cell biology.

In addition, we systematically evaluate the performance of 14 open-source and closed-source advanced LLMs on CellVerse.

🚀 Inference

We provide two formats for inference on CellVerse using APIs and vLLM.

Inference using APIs:

python ./evaluation/infer_api.py \
    --model_name "$Model" \
    --openai_api_key "$API" \
    --base_url "$URL" \
    --output_path "./results/response.json" \
    --dataset_path "./data/cta_scrna_full.json" 

Inference using vLLM:

python ./evaluation/infer_vllm.py \
    --model_name "$Model" \
    --output_path "./results/response.json" \
    --dataset_path "./data/cta_scrna_full.json" 

💪 Evaluation

After getting model responses (saved in output_path), you can extract the answer and calculate the metrics.

Here we provide an example of the extracted answer at ./results/ms_cta_response_deepseek_r1.json.

🏆 Leaderboard

#	Model	Type	Source	Date	Avg	CTA (scRNA-seq)	CTA (CITE-seq)	CTA (ASAP-seq)	DRP	PSA	PDA
1	DeepSeek-R1 🥇	Open	Link	2025-03	53.05	42.38	58.29	28.00	50.00	76.67	62.96
2	GPT-4.1-mini 🥈	Close	Link	2025-04	52.24	40.51	59.14	29.33	55.00	68.33	61.11
3	GPT-4o 🥉	Close	Link	2024-11	49.45	35.70	58.29	28.00	47.50	71.67	55.56
4	GPT-4.1	Close	Link	2025-04	49.41	37.83	61.43	28.22	49.38	73.33	46.30
5	LLaMA-3.3-70B	Open	Link	2024-12	45.86	32.75	52.57	22.00	43.75	66.67	57.41
6	DeepSeek-V3	Open	Link	2025-03	45.53	37.57	57.14	27.11	50.63	76.67	24.07
7	Qwen-2.5-72B	Open	Link	2024-09	43.66	24.73	50.29	28.44	50.00	73.33	35.19
8	Qwen-2.5-32B	Open	Link	2024-09	37.97	22.46	48.86	23.11	45.63	76.67	11.11
9	Qwen-2.5-7B	Open	Link	2024-09	35.16	13.77	30.86	10.67	49.38	76.67	29.63
10	GPT-4o-mini	Close	Link	2024-07	34.38	23.93	48.57	16.89	43.75	41.67	31.48
11	GPT-4	Close	Link	2023-06	18.57	35.16	53.43	21.56	1.25	0.00	0.00
12	C2S-Pythia-1B	Open	Link	2025-04	0.00	0.00	0.00	0.00	0.00	0.00	0.00
12	C2S-Pythia-410M	Open	Link	2024-09	0.00	0.00	0.00	0.00	0.00	0.00	0.00
12	C2S-Pythia-160M	Open	Link	2024-02	0.00	0.00	0.00	0.00	0.00	0.00	0.00

:white_check_mark: Citation

If you find CellVerse useful for your research and applications, please kindly cite using this BibTeX:

@misc{zhang2025cellverselargelanguagemodels,
      title={CellVerse: Do Large Language Models Really Understand Cell Biology?}, 
      author={Fan Zhang and Tianyu Liu and Zhihong Zhu and Hao Wu and Haixin Wang and Donghao Zhou and Yefeng Zheng and Kun Wang and Xian Wu and Pheng-Ann Heng},
      year={2025},
      eprint={2505.07865},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2505.07865}, 
}

🔥 Please contact zfkarl1998@gmail.com if you would like to contribute to the leaderboard or have any problems.