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scHyper:通过超图神经网络重建细胞间的通信

Wenying Li¹, Haiyun Wang¹, Jianping Zhao¹

¹School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China.

Briefings in bioinformatics

|September 14, 2024

概括

scHyper使用一种新的超图模型推断细胞间的通信. 该方法提供了全球网络视图,从单细胞RNA测序数据改进了复杂细胞间通信网络的分析.

科学领域:

计算生物学计算生物学
系统生物学系统生物学
生物信息学是一种生物信息学.

背景情况:

细胞与细胞之间的沟通对于多细胞生命和组织组织至关重要.
现有的从单细胞RNA测序 (scRNA-seq) 数据中推断细胞-细胞通信的方法往往缺乏全面的全球网络视角.
多层和复杂的细胞间相互作用需要先进的分析方法.

研究的目的:

开发一种新的计算方法,scHyper,以推断全球网络视图的细胞间通信.
用异构的超图结构来表示和分析复杂的细胞-细胞通信通路.
为了确定连接体-受体相互作用和细胞特异性表达对通信分数的影响.

主要方法:

scHyper使用异质超图来模拟连接体表达,受体表达和连接体-受体对之间的三方关系.
采用超图表示学习来量化基于节点属性和超边形形成的通信概率.
包含了丰富的数据集,包括多子单元复杂的连接体-受体对,以及用于识别显著细胞间通信的非参数测试.

主要成果:

scHyper与现有的细胞-细胞通信推断工具相比,表现出卓越的性能和功能.
对人类瘤微环境和免疫细胞的实验验证显示了scHyper的可靠性和独特的分析能力.
该方法有效地捕捉了高阶相互作用模式,克服了低阶相互作用模型的局限性.

结论:

scHyper提供了一种有效的策略,用于在细胞间通信网络中构建高阶交互模式.
这种方法可以更准确地解释细胞间通信的复杂性.
该方法通过提供细胞相互作用的全面全球网络视图来增强生物系统的分析.

关键词:

深度学习是一种深度学习.全球网络全球网络网络.超图神经网络的神经网络.细胞间通信是细胞间通信.一个单细胞RNA-seqq.

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