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使用对抗图对比学习的单细胞RNA-seq深度聚类

Le Van Vinh¹, Tran Nhat Quang², Lai Hoang Hiep¹

¹Faculty of Information Technology, HCMC University of Technology and Education, Vo Van Ngan Street, Ho Chi Minh City 700000, Vietnam.

Briefings in bioinformatics

|August 21, 2025

概括

这项研究引入了scAGCL,这是单细胞RNA测序 (scRNA-seq) 数据集群的新方法. scAGCL使用对抗图对比学习来改善细胞类型分类和标记基因的识别,其性能优于现有的算法.

科学领域:

计算生物学
基因组学
生物信息学

背景情况:

单细胞技术为生物洞察提供细胞分辨率.
准确的细胞类型分类对于分析单细胞RNA测序 (scRNA-seq) 数据至关重要.
由于数据特征,现有的scRNA-seq聚类方法面临计算挑战.

研究的目的:

提出使用对抗图对比学习进行scRNA-seq数据聚类的新方法.
在scRNA-seq数据分析中提高细胞类型分类的准确性和可扩展性.
开发一种方法,也可以支持细胞类型的标记基因识别.

主要方法:

提出的方法scAGCL构建了一个细胞-细胞图.
它使用对抗图对比学习对图结构和节点特征进行表示学习.
为了提高可扩展性,纳入了子图采样技术.

主要成果:

与七个最先进的算法相比,scAGCL在真实scRNA-seq数据集上表现出更高的性能.
这种方法有效地将细胞分成不同的类型.
对于确定细胞类型至关重要的标记基因的识别有助于scAGCL.

结论:

scAGCL为scRNA-seq数据集群提供了有效和可扩展的方法.
对抗图对比学习框架增强了细胞类型的分类.
这种方法有助于更深入地了解细胞异质性和基因表达模式.

关键词:

敌对的攻击集群化对比式学习图形神经网络一个细胞RNA-seq

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相关概念视频

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...