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

• URL: http://arxiv.org/abs/2505.00017v1
• Date: Thu, 24 Apr 2025 01:05:22 GMT
• Title: ReCellTy: Domain-specific knowledge graph retrieval-augmented LLMs workflow for single-cell annotation
• Authors: Dezheng Han, Yibin Jia, Ruxiao Chen, Wenjie Han, Shuaishuai Guo, Jianbo Wang,
• Abstract summary: We developed a graph structured feature marker database to retrieve entities linked to differential genes for cell reconstruction.<n>Our method improves human evaluation scores by up to 0.21 and semantic similarity by 6.1% across 11 tissue types.
• Score: 8.31906400360507
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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