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Hi-C3:一种基于统计推断的模型,用于重建高阶细胞-细胞通信网络

Yuyan Tong¹, Renhao Hong¹, Meng Li²

¹School of Mathematics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong, China.

Briefings in bioinformatics

|October 29, 2025

概括

多细胞生物依赖细胞-细胞通信 (CCC) 进行协调功能. 一个新的框架,Hi-C3,从单细胞RNA测序数据中推断出双向和高阶CCC模式,揭示出复杂的通信网络.

科学领域:

计算生物学计算生物学
系统生物学系统生物学
基因组学就是基因组学.

背景情况:

多细胞生物需要协调的细胞-细胞通信 (CCC) 来进行发育和功能.
目前使用单细胞RNA测序 (scRNA-seq) 的方法主要推断为对的联体受体相互作用 (LRIs).
生物过程通常涉及多种细胞类型的协调行动,需要超越对对相互作用的模型.

研究的目的:

开发一个统计框架,Hi-C3,用于从scRNA-seq数据中推断双向和高阶CCC模式.
模拟受体表达受多种联结体产生细胞类型的集体信号的影响.
在复杂的多细胞网络中确定关键的蜂通信枢纽.

主要方法:

开发了Hi-C3,一个使用网络扩散和流行病动态原则的统计推理框架.
模拟受体表达作为由集体信号调节的Poisson分布变量.
采用基于概率的预期最大化 (EM) 算法和修改的PageRank算法进行网络分析.

主要成果:

Hi-C3成功地推断出与最先进的方法可比的对联CCC.
该框架独特地揭示了复杂的高级CCC结构.
在Arabidopsis thaliana和结直肠癌数据集中,通过独立的生物和空间证据支持确定了关键的细胞通信枢纽.

结论:

Hi-C3提供了一个强大的统计模型和计算框架,用于发现更高阶的CCC.
该方法揭示了复杂的多细胞信号结构,经常被双向推断遗漏.
提供了关于细胞组织和发育和疾病中的通信网络的新见解.

关键词:

高阶细胞细胞通信.细胞间通信是细胞间的通信.最大的概率估计估计.简化的复杂的复杂.一个单细胞RNA-seqq.

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