注意引导的概率扩散模型用于从基因表达特征中生成细胞类型特定的基因调控网络
Shiyu Xu1, Na Yu2, Daoliang Zhang3
1Department of Bioinformatics, School of Life Sciences, Xuzhou Medical University, Xuzhou 221004, China.
Genes
|November 27, 2025
概括
新的深度学习框架Planet从单细胞RNA测序数据中构建细胞特异性基因调控网络 (GRNs). 它提供了基因调节和动态变化的系统级视图,提高了全球网络的一致性.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 基因调控网络 (GRNs) 通过基因转录控制细胞的身份和功能.
- 单细胞技术可以剖析特定细胞状态中的调节机制.
- 现有的计算方法经常独立推断局部监管相互作用,限制全球视角.
研究的目的:
- 提出Planet,一个用于从单细胞RNA测序 (scRNA-seq) 数据中构建细胞特异性GRNs的深度学习框架.
- 为了提供一个系统层面的看法,蛋白质调节器如何编排转录程序.
- 通过共同优化本地网络结构和基因表达特征,在全球范围内提高GRNs的结构一致性.
主要方法:
- 开发的星球,一个基于扩散模型的深度学习框架.
- 分解的GRN生成进入马科维亚进化阶段.
- 引入了三重混合注意力变压器,以捕捉跨扩散时间步骤的远程监管依赖.
- 采用快速采样策略进行加速推断.
主要成果:
- 星球在多个scRNA-seq数据集上实现了与最先进的方法相比具有竞争力的性能.
- 在重建的GRNs中证明了全球一致性的改善.
- 成功重建了小鼠肺部Cd8+Gzmk+T细胞的细胞类型特定GRN,识别了已知的和新的调节者.
- 与衰老相关的动态免疫调节变化.
结论:
- 星球为构建具有增强全球一致性的细胞特异GRNs提供了一个实用的框架.
- 提供了对现有的GRN推断方法的补充视角.
- 提供了对健康和疾病的调节动态的新见解,特别是在衰老的免疫细胞中.
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