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

Han, D.; Jia, Y.; Chen, R.; Han, W.; Guo, S.; Wang, J.

doi:10.1101/2025.04.23.650201

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bioRxiv · 10.1101/2025.04.23.650201

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

Han, D.·Jia, Y.·Chen, R.·Han, W.·Guo, S.·Wang, J.

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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.

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Han, D., Jia, Y., Chen, R., Han, W., Guo, S., Wang, J.. 2025-04-26. ReCellTy: Domain-specific knowledge graph retrieval-augmented LLMs workflow for single-cell annotation. https://doi.org/10.1101/2025.04.23.650201

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