scapGNN: 一个基于图形神经网络的框架,用于从单细胞多组数据中推断活性通路和基因模块
Xudong Han1,2, Bing Wang1,2, Chenghao Situ2
1State Key Laboratory of Reproductive Medicine and Offspring Health, School of Medicine, Southeast University, Nanjing, China.
PLoS biology
|November 13, 2023
概括
我们开发了scapGNN,这是一个图形神经网络框架,用于分析单细胞多omics数据. 该工具增强了途径活动评分和基因模块识别,以获得更深入的生物学见解.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 单细胞技术允许多omics分析,但提取功能性见解是具有挑战性的.
- 现有的方法难以应对单细胞多组数据的复杂性和稀疏性.
研究的目的:
- 为了介绍scapGNN,一个新的图形神经网络 (GNN) 框架用于分析单细胞多omics数据.
- 为了使单细胞路径活动的强有力的推断和细胞表型相关基因模块的识别.
主要方法:
- scapGNN将稀疏的单细胞特征转化为基因细胞关联网络.
- 使用图形神经网络架构进行数据集成和分析.
- 开发为一个灵活和可扩展的R包.
主要成果:
- 与最先进的方法相比,scapGNN显示出卓越的准确性,稳定性和可扩展性.
- 在细胞消毒,批量效应去除,聚类和轨迹推断中成功应用.
- 有效地识别细胞表型相关的基因模块和通路活动.
结论:
- scapGNN提供了一个强大的新分析平台,用于单细胞多omics研究.
- 促进在通路和网络层面更深入地了解细胞功能.
- 提供灵活的R包,可以集成到现有的单细胞分析工作流程中.
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