使用深度生成学习预测单剂和多剂量依赖基因表达的协议.
Derek E Bowman1, Vishal Panda2, Daniel Marri3
1Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, USA; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; College of Osteopathic Medicine, Michigan State University, East Lansing, MI, USA.
我们介绍scVIDR,一种变异自编码器 (VAE) 模型,用于在化学剂量下分析单细胞基因表达. 本协议详细说明了用于剂量反应研究的模型训练和预测基因表达的细节.
科学领域:
- 计算生物学是一种计算生物学.
- 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) 能够对细胞异质性的高分辨率分析.
- 建模基因表达对于理解细胞对刺激的反应至关重要.
- 现有的方法可能无法在单细胞数据中完全捕捉剂量依赖的效应.
研究的目的:
- 引入单细胞剂量响应变异推断 (scVIDR),一种针对scRNA-seq数据量身定制的VAE.
- 为利用scVIDR在各种化学扰动下建模基因表达提供一个协议.
- 为了促进在单细胞水平上对剂量依赖的生物反应的分析.
主要方法:
- 开发scVIDR,一个专门用于scRNA-seq.中剂量反应建模的VAE.
- 使用Docker来访问scVIDR工具的代码和数据管理.
- 训练scVIDR模型和预测基因表达模式的详细程序.
主要成果:
- 该 scVIDR 协议使单细胞基因表达的有效建模成为可能.
- 该方法允许预测基因表达的变化,以响应化学剂量.
- 使用Docker可确保scVIDR框架的可复制性和易于访问.
结论:
- scVIDR提供了一个强大的计算框架,用于分析scRNA-seq数据中的剂量依赖化学扰动.
- 该协议为研究人员提供了实施和应用scVIDR的实用指南.
- 这种方法增强了对细胞对化学治疗反应的理解.
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