从基于图形嵌入的单细胞转录组学推断基因调控网络
Yanglan Gan1, Jiacheng Yu1, Guangwei Xu1
1School of Computer Science and Technology, Donghua University, Shanghai 201620, China.
Bioinformatics (Oxford, England)
|May 29, 2024
概括
从单细胞RNA测序 (scRNA-seq) 数据中推断基因调控网络 (GRNs) 是一个挑战. 我们的IGEGRNS深度学习框架通过考虑全球基因关系和远程调节连接来改善GRN推断.
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:
- 基因调节网络 (GRNs) 对于理解生物过程至关重要.
- 从单细胞RNA测序 (scRNA-seq) 数据中推断GRNs存在挑战,原因是基因调控的动态和复杂性质.
- 现有的方法往往忽略了全球结构和远程监管关系.
研究的目的:
- 开发一个新的监督深度学习框架,IGEGRNS,用于从scRNA-seq数据中准确推断GRN.
- 通过整合全球网络结构和远程监管信息来解决现有方法的局限性.
- 通过改进GRN重建,增强对复杂生物过程的理解.
主要方法:
- 开发了IGEGRNS,这是一个使用图形嵌入的监督深度学习框架.
- 采用 GraphSAGE 来捕获上下文基因信息,通过将特征和邻近结构聚合到低维嵌入中.
- 利用Top-k聚合来识别有影响力的节点和卷积神经网络 (CNN) 来预测监管关系.
主要成果:
- 与其他九种监督和无监督方法相比,IGEGRNS框架显示出更高的性能.
- 在6个时间序列scRNA-seq数据集中推断基因调节网络的准确性提高.
- 成功捕获了全球网络结构和远程监管关系,改进了现有的方法.
结论:
- IGEGRNS提供了一种强大而有效的方法,可以从scRNA-seq数据中推断基因调节网络.
- 该框架能够整合全球网络拓和远程交互提供了显著的优势.
- 这一进步有助于更深入地了解基因调节和复杂的生物系统.
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