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Lin Li¹, Rui Xia^1,2, Wei Chen^1,2

¹Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.

Briefings in bioinformatics

|August 6, 2023

概括

我们开发了一种新方法,邻近交叉映射 (NME),以推断基因调节网络 (GRNs). NME从复杂的生物数据中准确地识别基因关系,有助于细胞状态的发现.

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:

基因调节网络 (GRNs) 对于理解细胞功能和状态至关重要.
由于数据噪声和非线性分子动力学,在GRNs中推断因果关系具有挑战性.

研究的目的:

引入一个新的因果标准,邻近交叉映射 (NME),用于强大的GRN推理.
评估NME在模拟和现实世界单细胞RNA测序 (scRNA-seq) 数据上的性能.
证明NME在识别细胞状态和改进下游分析方面的实用性.

主要方法:

开发了邻近交叉映射 (NME) 标准,以量化变量之间的连续因果关系.
将NME应用于稳态和时间序列数据,包括scRNA-seq数据集.
通过使用基准数据集,将NME的表现与现有的GRN推断方法进行比较.

主要成果:

与对基准数据集的现有方法相比,NME在推断GRN方面表现优异.
从scRNA-seq数据中,NME成功地推断出细胞类型特定的GRNs,并从scRNA-seq数据中确定了不同的细胞状态.
由NME推断的GRN提高了单细胞聚类和其他下游分析的准确性.

结论:

从复杂的生物数据中推断因果基因调节网络,NME提供了一种强大而准确的方法.
该方法有助于发现新的细胞类型/状态,并预测细胞类型特定的网络模块.
在连续因果关系中NME的基础为分析scRNA-seq数据中的基因相互作用提供了强大的框架.

关键词:

在 GRN GRN GRN 中.没有NEM,没有NEM.有关因果关系的因果关系交叉映射的变.一个单细胞的单细胞.

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