Learning Single-Cell Perturbation Responses using Neural Optimal Transport
| Autor: | Bunne, Charlotte, Stark, Stefan G., Gut, Gabriele, Sarabia del Castillo, Jacobo, Lehmann, Kjong-Van, Pelkmans, Lucas, Krause, Andreas, Rätsch, Gunnar |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Zdroj: | bioRxiv |
| DOI: | 10.3929/ethz-b-000524354 |
| Popis: | The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.Competing Interest StatementG.G. and L.P. have filed a patent on the 4i technology (patentWO2019207004A1). bioRxiv |
| Databáze: | OpenAIRE |
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