Learning single-cell perturbation responses using neural optimal transport

By Charlotte Bunne, Stefan G. Stark, Gabriele Gut, Jacobo Sarabia del Castillo, Kjong-Van Lehmann, Lucas Pelkmans, Andreas Krause, Gunnar Rätsch. bioRxiv, 2021

309 citations recorded by the source corpus.

CellOT combines the benefits of optimal transport and input convex neural architectures to directly learn and uncover maps between control and perturbed cell states at the single-cell level. 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 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.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b44faa0d29a0a0a5c8a1f0d1fa52c4b8b2d33eb0

Citations in the corpus