使用深度生成学习预测单剂量和多剂量依赖性基因表达的方案。
Protocol for predicting single- and multiple-dose-dependent gene expression using deep generative learning.
作者信息
Bowman Derek E, Panda Vishal, Marri Daniel, Kana Omar, Bhattacharya Sudin
机构信息
Department 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.
Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
出版信息
STAR Protoc. 2025 Jul 11;6(3):103932. doi: 10.1016/j.xpro.2025.103932.
Variational autoencoders (VAEs) can be used to model the gene expression space of single-cell RNA sequencing (scRNA-seq) data. Here, we present a protocol for using single-cell variational inference of dose response (scVIDR), a VAE designed to model single-cell gene expression for dose-dependent chemical perturbations. We describe steps to access the scVIDR code and data using a containerization application called Docker. We then detail procedures for training the sVIDR model and predicting gene expression. For complete details on the use and execution of this protocol, please refer to Kana et al..
变分自编码器（VAEs）可用于对单细胞RNA测序（scRNA-seq）数据的基因表达空间进行建模。在此，我们展示了一种使用单细胞剂量反应变分推理（scVIDR）的方案，scVIDR是一种VAE，旨在对剂量依赖性化学扰动的单细胞基因表达进行建模。我们描述了使用名为Docker的容器化应用程序访问scVIDR代码和数据的步骤。然后，我们详细介绍了训练sVIDR模型和预测基因表达的程序。有关此方案的使用和执行的完整详细信息，请参考卡纳等人的研究。
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