Generative modeling of single-cell gene expression for dose-dependent chemical perturbations

• Published in: Patterns (New York, N.Y.) N.Y.), 2023-08, Vol.4 (8), p.100817-100817, Article 100817
• Main Authors: Kana, Omar, Nault, Rance, Filipovic, David, Marri, Daniel, Zacharewski, Tim, Bhattacharya, Sudin
• Format: Article
• Language: English
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2023.100817

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.