scSHEFT可以通过双对齐实现从scRNA-seq到scATAC-seq的多组学标签传输
Zhitao Huang1, Ruiqing Zheng1, Pengzhen Jia1
1School of Computer Science and Engineering, University, Changsha, Hunan, 410083, China.
Genome research
|January 20, 2026
概括
我们开发了scSHEFT,这是一种用于单细胞多组学标签转移的新工具. 它有效地弥合了基因表达和染色体可访问性等异质数据类型,改善了细胞类型的识别和发现.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞多组学数据正在迅速出现,推动了标签转移的趋势,从注释良好的单细胞RNA测序 (scRNA-seq) 到注释较少的数据,如单细胞转化酶可访问染色体测序 (scATAC-seq) 的测试.
- 这种跨基因的标签转移旨在通过利用基因表达特征来注释细胞类型,包括新型细胞.
- scRNA-seq和scATAC-seq之间的异质性为准确的细胞类型识别带来了重大挑战,并阻碍了新型细胞类型的发现.
研究的目的:
- 介绍scSHEFT,这是一种新的计算工具,旨在克服跨omics标签转移的挑战.
- 为了使基因表达计数数据,峰值计数数据和基因活动得分能够同时考虑,以弥合异质特征.
- 为了提高单细胞多组学数据中细胞类型注释的准确性和范围.
主要方法:
- scSHEFT将scATAC-seq数据转换为基因活动分数,使用先前的知识来协调特征.
- 原始ATAC-seq嵌入被纳入以减轻特征转换期间的信息损失.
- 采用双重对齐策略,将基于的方法用于经济内部对齐和基于对比的策略用于保持经济内部异质性.
主要成果:
- 与11种最先进的方法相比,scSHEFT表现出卓越的性能.
- 该工具在七个不同的数据集中进行了基准测试,展示了其有效性.
- scSHEFT在处理不同尺度和技术噪音水平的数据集方面表现出色.
结论:
- scSHEFT提供了一个强大的解决方案,用于跨异质单细胞多组数据的标签传输.
- 该方法增强了细胞类型的识别,并促进了新型细胞类型的发现.
- scSHEFT在分析单细胞多组数据集方面取得了重大进展.
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