CellMaster：单细胞分析中的协同细胞类型标注

摘要

單細胞RNA測序（scRNA-seq）能夠實現複雜組織的圖譜級分析，揭示稀有譜系和瞬時狀態。然而，由於標記物具有組織和狀態特異性，且新發現的細胞狀態缺乏參考數據，如何準確賦予細胞生物學意義的身份標註仍是瓶頸。本文提出CellMaster——一種模擬專家實踐的零樣本細胞類型註釋人工智能代理。與現有自動化工具不同，CellMaster利用大型語言模型（如GPT-4o）編碼的知識進行即時註釋，並提供可解釋的判定依據，無需預訓練或固定標記數據庫。在涵蓋8種組織的9個數據集中，CellMaster在自動模式下較最佳基準方法（包括CellTypist和scTab）準確率提升7.1%。引入人機協同優化後，優勢擴大至18.6%，其中亞群細胞註釋準確率提升達22.1%。該系統在基準方法常失效的稀有及新穎細胞狀態註釋中表現尤為突出。源代碼及網絡應用程序詳見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}.

CellMaster：单细胞分析中的协同细胞类型标注

CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis

摘要

Support