从时间序列scRNA-seq数据推断基因调节网络,通过GRANGER因果反复自编码器
Liang Chen1, Madison Dautle2, Ruoying Gao1
1College of Computer and Information Engineering, Tianjin Normal University, 393 Binshui W Ave, Tianjin, Tianjin 300387, China.
Briefings in bioinformatics
|March 10, 2025
概括
GRANGER是一种新的深度学习方法,可以从杂,稀疏的时间序列单细胞RNA测序 (scRNA-seq) 数据中准确地推断基因调控网络 (GRNs). 它的性能优于现有的方法,并揭示了小鼠大脑中的新调节相互作用.
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:
- 单细胞RNA测序 (scRNA-seq) 为推断基因调控网络 (GRNs) 产生了有价值的数据.
- 现有的GRN推断方法与时间序列scRNA-seq数据固有的噪声和稀疏性作斗争.
- 跨时间点的动态变化和数据稀疏性对准确的GRN推理构成重大挑战.
研究的目的:
- 从时间序列单细胞RNA测序 (scRNA-seq) 数据中推断基因调控网络 (GRNs) 的先进方法.
- 为解决GRN推断的scRNA-seq数据中的噪声,稀疏性和动态变化的挑战.
- 为发现新型基因调节关系提供强大而准确的工具.
主要方法:
- 引入了GRANGER,一种无监督的深度学习方法,集成反复变量自编码器,GRANGER因果关系,稀疏性诱导惩罚和基于负二项式 (NB) 的损失函数.
- 利用基于NB的损失函数和稀疏性诱导处罚来有效处理掉机噪声和数据稀疏性.
- 使用GRANGER因果关系来捕捉时间依赖性和推断调节相互作用.
主要成果:
- GRANGER在推断GRNs方面表现出优异的性能,与对比数据集的八种既定方法相比.
- 该方法对高水平的脱落噪声显示出显著的稳定性,并有效地解决了数据稀疏性.
- 对小鼠全脑scRNA-seq数据的应用确定了关键转录调节者的新型GRN,包括E2f7,Gbx1,Sox10,Prox1和Onecut2.2.
结论:
- GRANGER是一种高效的深度学习工具,用于从时间序列scRNA-seq数据中推断基因调节网络.
- 该方法成功地确定了已知的和新的调节相互作用,为细胞机制和疾病提供了宝贵的见解.
- 在发现复杂的基因调节关系方面,GRANGER对现实世界的应用具有重大前景.
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