重建动态基因调控网络使用时间序列scRNA-Seq数据的f-分歧
Yunge Wang1, Lingling Zhang2, Tong Si3
1Department of Mathematics and Statistics, Saint Louis University, St. Louis, MO 63103, USA.
Current issues in molecular biology
|July 23, 2025
概括
我们开发了f-DyGRN,这是一种从单细胞RNA测序数据推断动态基因调控网络的新方法. 它准确地捕捉了基因表达的变化随着时间的推移,超越现有的方法.
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:
- 从时间序列单细胞RNA测序 (scRNA-seq) 数据中推断动态基因调控网络 (GRNs) 是复杂的.
- 现有的方法与scRNA-seq数据的稀疏性,脱落和异质性作斗争,并且经常无法捕捉单细胞水平的动态调节变化.
研究的目的:
- 提出一种新的方法,即基于f-分歧的动态基因调控网络推断 (f-DyGRN),用于从scRNA-seq数据中重建时间变化的GRN.
- 为了解决当前方法的局限性,以捕捉单个细胞随时间变化的动态监管变化.
主要方法:
- 利用f-分歧量化个体细胞的时间基因表达变异.
- 整合了第一阶格兰杰因果关系模型与规范化和部分相关性分析.
- 采用移动窗口策略来捕捉不同时间阶段的动态基因相互作用.
主要成果:
- 与现有方法相比,f-DyGRN在重建动态监管网络方面表现优越.
- 该方法成功分析了来自THP-1细胞的模拟和真实scRNA-seq数据.
- 绩效取决于选择的f-差异度量.
结论:
- f-DyGRN提供了一个强大的方法,可以从时间序列scRNA-seq数据中推断动态基因调节网络.
- 该方法有效地处理scRNA-seq数据所带来的独特挑战.
- 这一进步有助于理解单细胞水平上的动态生物过程.
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