Learning single-cell perturbation responses using neural optimal transport

• DOI: 10.1038/s41592-023-01969-x
• Publication Date: 2023-09-28T16:02:40Z
• Authors: Charlotte Bunne; Stefan G. Stark; Gabriele Gut; Jacobo Sarabia de...; Mitch Levesque; Kjong-Van Lehmann; Lucas Pelkmans; Andreas Krause; Gunnar Rätsch

AbstractUnderstanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-β and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.