Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Abstract

Abstract Understanding 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 nonperturbed cells. Here we leverage the theory of optimal transport and the recent advent of convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by coupling these unpaired distributions. We achieve this alignment with a learned transport map that allows us to infer the treatment responses of unseen untreated cells. 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 in unseen settings by (a) predicting the scRNA-seq responses of heldout lupus patients exposed to IFN-β and (b) modeling the hematopoietic developmental trajectories of different subpopulations. We expect CellOT to lay the grounds for delineating the causes of heterogeneous single-cell responses to perturbations and predicting patient-specific drug response landscapes instead of population averages.