CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910022005424128 |
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| author | Wang, Zhen Gao, Yiming Liu, Jieyuan Ma, Enze Chen, Jefferson Antkowiak, Mark Hu, Mengzhou Kong, JungHo Pratt, Dexter Hu, Zhiting Wang, Wei Ideker, Trey Xing, Eric P. |
| author_facet | Wang, Zhen Gao, Yiming Liu, Jieyuan Ma, Enze Chen, Jefferson Antkowiak, Mark Hu, Mengzhou Kong, JungHo Pratt, Dexter Hu, Zhiting Wang, Wei Ideker, Trey Xing, Eric P. |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13346 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis Wang, Zhen Gao, Yiming Liu, Jieyuan Ma, Enze Chen, Jefferson Antkowiak, Mark Hu, Mengzhou Kong, JungHo Pratt, Dexter Hu, Zhiting Wang, Wei Ideker, Trey Xing, Eric P. Genomics Artificial Intelligence Computer Vision and Pattern Recognition 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}. |
| title | CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis |
| topic | Genomics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.13346 |