LLM4Cell：面向单细胞生物学的大型语言与智能体模型综述

LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell Biology

October 9, 2025

作者: Sajib Acharjee Dip, Adrika Zafor, Bikash Kumar Paul, Uddip Acharjee Shuvo, Muhit Islam Emon, Xuan Wang, Liqing Zhang

cs.AI

摘要

大型语言模型（LLMs）及新兴的代理框架正通过实现自然语言推理、生成式注释和多模态数据整合，逐步革新单细胞生物学领域。然而，这一进展在数据模态、架构及评估标准方面仍显分散。LLM4Cell首次对58个专为单细胞研究开发的基础模型和代理模型进行了统一综述，涵盖RNA、ATAC、多组学和空间模态。我们将这些方法划分为五大类别——基础模型、文本桥梁模型、空间模型、多模态模型、表观基因组模型及代理模型，并将其映射至包括注释、轨迹与扰动建模、药物反应预测在内的八大关键分析任务。基于40多个公共数据集，我们分析了基准适用性、数据多样性以及伦理或可扩展性限制，并从生物基础性、多组学一致性、公平性、隐私性和可解释性等10个领域维度对模型进行了评估。通过关联数据集、模型与评估领域，LLM4Cell首次提供了语言驱动单细胞智能的综合视角，并阐明了在可解释性、标准化及可信模型开发方面面临的开放挑战。

English

Large language models (LLMs) and emerging agentic frameworks are beginning to transform single-cell biology by enabling natural-language reasoning, generative annotation, and multimodal data integration. However, progress remains fragmented across data modalities, architectures, and evaluation standards. LLM4Cell presents the first unified survey of 58 foundation and agentic models developed for single-cell research, spanning RNA, ATAC, multi-omic, and spatial modalities. We categorize these methods into five families-foundation, text-bridge, spatial, multimodal, epigenomic, and agentic-and map them to eight key analytical tasks including annotation, trajectory and perturbation modeling, and drug-response prediction. Drawing on over 40 public datasets, we analyze benchmark suitability, data diversity, and ethical or scalability constraints, and evaluate models across 10 domain dimensions covering biological grounding, multi-omics alignment, fairness, privacy, and explainability. By linking datasets, models, and evaluation domains, LLM4Cell provides the first integrated view of language-driven single-cell intelligence and outlines open challenges in interpretability, standardization, and trustworthy model development.