在单细胞RNA-Seq中对细胞类型进行大型语言模型的基准测试
Tianxiang Xiao1,2,3,4, Dezhi Hua5, Yanan Wang5
1State Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 17 Longxin Road, Panlong District, Kunming, Yunnan 650201, China.
Briefings in bioinformatics
|December 15, 2025
概括
大型语言模型 (LLM) 通过优于传统工具,显著改善单细胞RNA测序 (scRNA-seq) 中的细胞类型注释. 使用顶级LLM的整体策略为细胞亚型识别提供了最先进的准确性.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞RNA测序 (scRNA-seq) 产生用于细胞类型注释的高维数据.
- 目前使用大型语言模型 (LLM) 进行细胞类型注释的方法缺乏系统的框架和全面的性能评估.
- 传统的生物信息学工具可能会与scRNA-seq数据的复杂性和细粒度作斗争.
研究的目的:
- 系统地将LLM与scRNA-seq细胞类型注释的传统工具进行比较.
- 确定利用LLM的最佳策略,包括标记基因选择和排名.
- 为高性能电池类型注释开发一种强大且易于使用的解决方案.
主要方法:
- 基准测试七个领先的LLM和三个传统的生物信息学工具.
- 利用34个不同的人类和小鼠scRNA-seq数据集.
- 通过统计学上显著的标记基因优化注释准确度,按log2折变化排名.
主要成果:
- 与传统方法相比,LLM在细胞类型注释方面表现优越.
- 表现最好的LLM包括Kimi-k2,GPT-5,Claude-4.1和Grok-4. 这两种类型.
- 精英组合策略实现了最先进的准确性,特别是在细粒细胞亚型中.
结论:
- 在scRNA-seq研究中,LLM代表了细胞类型注释的重大进步.
- 开发的DeepCellSeek包提供了一个经过验证,高性能和用户友好的解决方案.
- 这项工作建立了将LLM整合到单细胞基因组学中的路线图,以加强生物发现.
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