CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
Authors: name: Zhen Wang, name: Yiming Gao, name: Jieyuan Liu, name: Enze Ma, name: Jefferson Chen, name: Mark Antkowiak, name: Mengzhou Hu, name: JungHo Kong, name: Dexter Pratt, name: Zhiting Hu, name: Wei Wang, name: Trey Ideker, name: Eric P. Xing
Organization: University of California at San Diego

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}.