单细胞测序揭示了细胞对化学干扰的异质性反应。然而，测试所有相关的细胞类型、化学物质和剂量的组合是一项艰巨的任务。一种称为变分自编码器（VAEs）的深度生成学习形式在预测单剂量的单细胞基因表达干扰方面已经取得了有效的结果。在这里，我们引入了基于VAE的单细胞剂量响应变分推断（scVIDR），该模型比现有模型更好地预测了单剂量和多剂量的细胞反应。我们展示了scVIDR可以预测小鼠肝细胞、人类血液细胞和癌细胞系的剂量依赖性基因表达。我们使用回归模型对scVIDR的潜在空间进行了生物解释，并使用scVIDR根据对化学干扰的敏感性对个别细胞进行排序，为每个细胞分配了一个“伪剂量”值。我们预期scVIDR可以帮助减少对组织、化学物质和剂量的重复动物实验的需求。 © 2023 作者们

Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.© 2023 The Author(s).