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通过从scRNA-seq数据构建细胞间相互作用的图像表示来增强基因调控网络的深度学习推断

Qingyue Wei, Md Tauhidul Islam, Wei Emma Wu

IEEE journal of biomedical and health informatics

|October 6, 2025

概括

本研究介绍了CelloGraph,这是一种新的深度学习方法,用于从单细胞RNA测序 (scRNA-seq) 数据中推断基因调节网络 (GRNs). 它准确地绘制基因表达模式,以提高对生物调节的理解.

科学领域:

基因组学和系统生物学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

基因调节网络 (GRNs) 对于理解生物过程和疾病中的基因相互作用至关重要.
单细胞RNA测序 (scRNA-seq) 提供了高分辨率的基因表达数据,但由于数据的复杂性,对GRN推断提出了挑战.
现有的GRN推断方法与scRNA-seq数据固有的规模和异质性作斗争.

研究的目的:

使用scRNA-seq数据开发一个高精度和计算效率的GRN推理策略.
利用深度学习来分析细胞间相互作用和基因表达模式.
克服当前处理复杂scRNA-seq数据集的方法的局限性.

主要方法:

开发了一种新的空间语义图像表示,CelloGraph,以可视化基于细胞与细胞相互作用的细胞间基因表达.
一个定制的卷积神经网络 (CNN) 被设计用于分析CelloGraphs并识别GRN推断的模式.
该方法整合了细胞相互作用信息和深度学习,以实现强大的GRN重建.

主要成果:

细胞图表表现有效地捕捉了细胞与细胞相互作用的动态.
美国有线电视新闻网成功地在CelloGraphs中发现了歧视性模式,以获得准确的GRN推断.
拟议的方法在各种生物医学数据集上表现出高精度和计算效率.

结论:

CelloGraph-CNN策略为基于scRNA-seq的GRN推断提供了一个有效的解决方案.
这种方法提高了对基因表达调节和生物机制的理解.
该方法提供了一个强大的工具来破译复杂的生物系统和疾病的病原性.

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