scRegulate:从基因表达的转录因子活性单细胞调节嵌入的变异推断
Mehrdad Zandigohar1, Jalees Rehman1,2, Yang Dai1
1Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, United States.
新的深度学习工具 scRegulate 从单细胞RNA测序数据中准确推断出转录因子 (TF) 活动和基因调控网络. 它整合了生物知识,以获得动态的,特定于环境的见解,在速度和准确性方面超越现有方法.
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:
- 从单细胞RNA测序 (scRNA-seq) 中准确推断转录因子 (TF) 活性至关重要,但具有挑战性.
- 现有的方法通常依赖于静态数据库和决定性假设,限制了它们捕捉动态监管相互作用的能力.
- 需要采用将先前的生物学知识与基于数据的推断相结合的方法,以便对特定环境的TF活动进行估计.
研究的目的:
- 开发一个新的生成深度学习框架,scRegulate,用于从scRNA-seq数据中估计TF活动和基因调控网络 (GRNs).
- 通过将结构化生物约束与概率潜伏空间模型相结合,解决现有方法的局限性.
- 为分析转录调节提供可扩展,可解释和生物学基础的工具.
主要方法:
- 开发了scRegulate,这是一个使用变化推理的生成深度学习框架.
- 综合实验TF-目标基因关系和结构化生物约束.
- 使用了一个概率潜伏空间模型,适应输入scRNA-seq数据.
主要成果:
- scRegulate在公开和合成数据集的基准分析研究中表现出卓越的表现.
- 该框架准确地总结了来自Perturb-seq数据的TF敲击效应.
- 应用于人类PBMC数据,scRegulate推断了细胞类型特定的GRNs和确定了关键的调节途径.
- scRegulate捕获了转录异质性,用于准确的细胞类型聚类,并且比基线方法显著提高了速度.
结论:
- scRegulate是一个强大的,可解释和可扩展的框架,用于从单细胞转录组学推断TF活动和GRNs.
- 该方法有效地将先前的生物学知识与数据驱动的推理集成为动态监管分析.
- 与现有的方法相比,scRegulate在准确性,效率和生物洞察力方面具有显著的优势.
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