CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
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}.
6994d4998d17d1ee8c10eb7b
Zhen Wang
claimed_verified
2026-02-18T09:07:49.149Z
64a833d2f152bba4b550c913
/avatars/cff37a427c01c6b6691f588481d96416.svg
Zhen Wang
zhenwang9102
user
zhenwang9102
6994d4998d17d1ee8c10eb7c
Yiming Gao
6994d4998d17d1ee8c10eb7d
Jieyuan Liu
6994d4998d17d1ee8c10eb7e
Enze Ma
6994d4998d17d1ee8c10eb7f
Jefferson Chen
6994d4998d17d1ee8c10eb80
Mark Antkowiak
6994d4998d17d1ee8c10eb81
Mengzhou Hu
6994d4998d17d1ee8c10eb82
JungHo Kong
6994d4998d17d1ee8c10eb83
Dexter Pratt
6994d4998d17d1ee8c10eb84
Zhiting Hu
6994d4998d17d1ee8c10eb85
Wei Wang
6994d4998d17d1ee8c10eb86
Trey Ideker
6994d4998d17d1ee8c10eb87
Eric P. Xing
CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
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 uses LLM-encoded knowledge for zero-shot cell-type annotation in single-cell RNA sequencing, improving accuracy over existing tools through interpretable rationales without pre-training.
single-cell RNA-seq cell-type annotation LLM-encoded knowledge zero-shot learning interpretable rationales CellTypist scTab