基于双GCN的深度聚类与三重对比,用于ScRNA-seq数据分析
LinJie Wang1, Wei Li2, WeiDong Xie1
1School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
Computational biology and chemistry
|July 24, 2023
概括
这项研究介绍了scDGDC,这是一种用于单细胞RNA测序 (ScRNA-seq) 数据的新型深度聚类方法. scDGDC通过提高缩小维度和细胞聚类性能来增强基因表达分析.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 单细胞RNA测序 (ScRNA-seq) 提供了高分辨率的基因表达数据.
- 聚类和缩小维度对于ScRNA-seq数据分析至关重要.
- 现有的基于图形的深度集群方法可能会忽略节点分布,并遭受过度平滑,影响嵌入质量.
研究的目的:
- 为ScRNA-seq数据提出一种基于双GCN的新深度聚类方法,使用三重对比 (scDGDC).
- 增强ScrRNA-seq数据分析中的拓和分布信息的捕获.
- 为了提高减小维度和聚类任务的性能.
主要方法:
- 开发了一种基于双GCN的编码器,以捕获全面的拓信息.
- 集成的三重对比,以减轻图形卷积网络 (GCN) 中常见的过度平滑问题.
- 在八个现实世界ScRNA-seq数据集上评估了scDGDC.
主要成果:
- scDGDC在缩小维度和聚类任务方面表现出色.
- 双GCN编码器有效地捕获了更丰富的拓信息.
- 三重对比成功地减少了GCN过度光滑,从而改善了嵌入表示.
- 该方法显示了对参数变化的高稳定性.
结论:
- scDGDC通过整合拓和分布信息,提供了一种有效的方法来分析ScRNA-seq数据.
- 拟议的方法解决了现有的基于图形的深度集群算法的局限性.
- scDGDC为ScRNA-seq的维度缩小和集群提供了强大的高性能解决方案.
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