CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis

• URL: http://arxiv.org/abs/2602.13346v1
• Date: Thu, 12 Feb 2026 20:20:22 GMT
• Title: CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
• Authors: Zhen Wang, Yiming Gao, Jieyuan Liu, Enze Ma, Jefferson Chen, Mark Antkowiak, Mengzhou Hu, JungHo Kong, Dexter Pratt, Zhiting Hu, Wei Wang, Trey Ideker, Eric P. Xing,
• Abstract summary: We present CellMaster, an AI agent that mimics expert practice for zero-shot cell-type annotation.<n>Across 9 datasets spanning 8 tissues, CellMaster improved accuracy by 7.1% over best-performing baselines in automatic mode.<n>The system demonstrates particular strength in rare and novel cell states where baselines often fail.
• Score: 35.57672494910454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \href{https://github.com/AnonymousGym/CellMaster}{https://github.com/AnonymousGym/CellMaster}.

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