LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell
  Biology
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.
68ece822b0aa732b88f31f8a
Sajib Acharjee Dip
68ece822b0aa732b88f31f8b
Adrika Zafor
68ece822b0aa732b88f31f8c
Bikash Kumar Paul
68ece822b0aa732b88f31f8d
Uddip Acharjee Shuvo
68ece822b0aa732b88f31f8e
Muhit Islam Emon
68ece822b0aa732b88f31f8f
Xuan Wang
68ece822b0aa732b88f31f90
Liqing Zhang
LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell
  Biology
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.
LLM4Cell surveys 58 foundation and agentic models for single-cell biology, categorizing them into families and evaluating them across various analytical tasks and domain dimensions.
large language models LLM4Cell single-cell biology natural-language reasoning generative annotation multimodal data integration RNA ATAC multi-omic spatial modalities text-bridge trajectory modeling perturbation modeling drug-response prediction benchmark suitability data diversity ethical constraints scalability constraints biological grounding multi-omics alignment fairness privacy explainability interpretability standardization trustworthy model development