scMGCL:准确和高效的整合表示单细胞多omics数据的数据
Zhenglong Cheng1, Risheng Lu1, Shixiong Zhang1
1School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
Bioinformatics (Oxford, England)
|July 9, 2025
概括
我们开发了scMGCL,这是一种用于整合单细胞ATAC-seq和RNA-seq数据的图形对比学习方法. 这种方法增强了细胞类型聚类和多omics分析的计算效率.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 单细胞多组体数据集成对于理解细胞异质性和疾病至关重要.
- 整合ATAC-seq和RNA-seq等多种数据模式存在重大挑战.
- 现有的方法难以稳定地结合来自不同单细胞试验的信息.
研究的目的:
- 介绍scMGCL,一个新的图形对比学习框架,用于单细胞多omics数据集成.
- 为了实现单细胞ATAC-seq和RNA-seq数据的稳健集成.
- 学习共享的表示,同时保持模式特定的特征.
主要方法:
- scMGCL使用了一个图形对比学习框架.
- 它在细胞相似性图表上采用自我监督学习.
- 交叉模式的图形结构被用作彼此的增强.
主要成果:
- scMGCL在细胞类型聚类和标签转移方面表现优于最先进的方法.
- 它证明了标记物-基因相关性的优越保存.
- 该框架显著提高了计算效率,减少了运行时间和内存使用量.
结论:
- scMGCL提供了一个强大而高效的工具,用于整合单细胞ATAC-seq和RNA-seq数据.
- 该方法有助于更深入地探索细胞类型相似性和功能一致性.
- 这一框架推动了单细胞多omics数据分析领域的发展.
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