一个全面的对比研究计算工具的跨-omics标签转移从单细胞RNA到ATAC数据的综合基准研究
Yuge Wang1, Hongyu Zhao1,2,3
1Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, 06511, USA.
G3 (Bethesda, Md.)
|February 8, 2026
概括
这项研究对27种单细胞ATAC测序 (scATAC-seq) 细胞类型注释的计算工具进行了基准测试. 桥和GLUE在配对数据中表现出色,而bindSC和GLUE在未配对数据中总体表现最好.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 单细胞分析 单细胞分析
背景情况:
- 单细胞染色体可访问性 (scATAC-seq) 对于理解发育和疾病中的基因调节至关重要.
- 准确的细胞类型注释对于解释复杂组织细胞性至关重要.
- 对于将标签从单细胞RNA测序 (scRNA-seq) 转移到scATAC-seq数据的方法有限.
研究的目的:
- 为 scATAC-seq 细胞类型注释提供 27 种计算工具进行全面的比较.
- 为了评估工具性能,使用人类和小鼠组织的配对和不配对scRNA-seq和scATAC-seq数据来评估工具性能.
- 确定跨模式标签转移的最佳方法,并评估影响性能的因素.
主要方法:
- 对27种用于scATAC-seq标签注释的计算工具进行比较.
- 利用来自不同人类和小鼠组织的配对和不配对单细胞RNA和ATAC测序数据.
- 基于预测准确性,可扩展性 (时间和内存效率) 和数据特征的影响来评估性能.
主要成果:
- 当可用于标签转移的高质量配对数据可用时,Bridge和GLUE表现最好.
- bindSC和GLUE在未配对数据中显示出总体最高的预测准确性.
- 数据不平衡,跨学科不相似性和半监督策略等因素对业绩产生了负面影响.
- 像GLUE这样的桥梁和深度学习方法展示了卓越的可扩展性.
结论:
- 根据数据的可用性,建议在scATAC-seq细胞类型注释中使用桥,GLUE和bindSC.
- 峰值级信息对于精确的注释至关重要,超出了基因活动.
- 未来的scATAC-seq方法的开发应该考虑数据特征和可扩展性.
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