ReCellTy: Domain-specific knowledge graph retrieval-augmented LLMs workflow for single-cell annotation

These authors contributed equally to this work. Abstract To enable precise and fully automated cell type annotation with large language models (LLMs), we developed a graph-structured feature-marker database to retrieve entities linked to differential genes for cell reconstruction. We further designed a multi-task workflow to optimize the annotation process. Compared to general-purpose LLMs, our method improves human evaluation scores by up to 0.21 and semantic similarity by 6.1% across 11 tissue types, while more closely aligning with the cognitive logic of manual annotation. Keywords: Cell type annotation, Graph RAG, Large language models, Graph data curation, Multi-task workflow, scRNA-seq In single-cell RNA sequencing analysis, achieving precise cell type annotation through manual labeling typically requires two key steps: annotators retrieve relevant marker genes and integrate this information with their domain expertise to make informed decisions.