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在单细胞RNA测序数据中,GraphComm使用基于图形的深度学习方法预测细胞与细胞的通信

Emily So^1,2,3, Sikander Hayat⁴, Sisira Kadambat Nair¹

¹Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.

Scientific reports

|October 22, 2025

概括

GraphComm是一种新的深度学习方法,通过整合细胞内信号和空间信息来增强单细胞RNA测序数据中的细胞-细胞通信 (CCC) 预测.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

细胞-细胞通信 (CCC) 对于协调健康和疾病中的细胞功能至关重要.
目前的单细胞技术提供了高分辨率,但难以捕捉复杂的细胞内相互作用和影响CCC的途径效应.
现有的方法往往无法推断总体信号模式并整合空间细胞维度.

研究的目的:

开发一种新的基于图形的深度学习方法,GraphComm,用于预测单细胞RNA测序 (scRNAseq) 数据集中的CCC.
通过结合详细的细胞内信号模式和细胞位置来改善CCC推断.
为了使转录基因数据作为复杂的网络的探索,补充基因表达与配体-受体相互作用.

主要方法:

开发了GraphComm,这是一个基于图形的深度学习框架,用于CCC预测.
集成的基因表达数据,包括关于细胞对连接体和受体相互作用的信息.
利用超过3万个蛋白质相互作用对的数据库捕获细胞内信号模式和细胞位置.

主要成果:

在scRNAseq数据集中,GraphComm成功地预测了生物相关的CCC.
该方法在具有化学或遗传干扰的数据集中证明了改进的CCC推断.
GraphComm有效地分析了包含空间单元信息的数据集.

结论:

GraphComm代表了从单细胞转录基因数据中预测细胞间通信的重大进步.
该方法能够整合细胞内信号和空间数据,提高了对复杂细胞交叉通话的理解.
GraphComm提供了一种强大的工具,可以在各种生物环境中剖析细胞通信模式.

关键词:

细胞细胞相互作用深度学习是一种深度学习.图表注意力网络的图表.图形神经网络是一个神经网络.单细胞RNA是一种单细胞RNA.

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