细胞大师：单细胞分析中的协作式细胞类型注释

摘要

单细胞RNA测序（scRNA-seq）能够实现复杂组织的图谱级解析，揭示稀有谱系和短暂状态。然而，由于标记基因具有组织和状态依赖性，且新细胞状态缺乏参考标准，如何准确界定具有生物学意义的细胞身份仍是瓶颈。我们推出CellMaster人工智能代理，该系统通过模拟专家决策模式实现零样本细胞类型注释。与现有自动化工具不同，CellMaster利用大型语言模型（如GPT-4o）内嵌的知识体系，无需预训练或固定标记数据库即可实现实时注释，并提供可解释的判定依据。在涵盖8种组织的9个数据集测试中，CellMaster在自动模式下较最优基线方法（包括CellTypist和scTab）准确率提升7.1%。引入人机协同优化后，优势扩大至18.6%，其中亚群细胞注释准确率提升达22.1%。该系统在基线方法常失效的稀有及新型细胞状态识别方面表现尤为突出。源代码及网络应用详见https://github.com/AnonymousGym/CellMaster。

English

Single-cell RNA-seq (scRNA-seq) enables atlas-scale profiling of complex tissues, revealing rare lineages and transient states. Yet, assigning biologically valid cell identities remains a bottleneck because markers are tissue- and state-dependent, and novel states lack references. We present CellMaster, an AI agent that mimics expert practice for zero-shot cell-type annotation. Unlike existing automated tools, CellMaster leverages LLM-encoded knowledge (e.g., GPT-4o) to perform on-the-fly annotation with interpretable rationales, without pre-training or fixed marker databases. Across 9 datasets spanning 8 tissues, CellMaster improved accuracy by 7.1% over best-performing baselines (including CellTypist and scTab) in automatic mode. With human-in-the-loop refinement, this advantage increased to 18.6%, with a 22.1% gain on subtype populations. The system demonstrates particular strength in rare and novel cell states where baselines often fail. Source code and the web application are available at https://github.com/AnonymousGym/CellMaster{https://github.com/AnonymousGym/CellMaster}.

细胞大师：单细胞分析中的协作式细胞类型注释

CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis

摘要

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