Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Bunne, C.; Stark, S. G.; Gut, G.; Sarabia del Castillo, J.; Lehmann, K.-V.; Pelkmans, L.; Krause, A.; Ratsch, G.

doi:10.1101/2021.12.15.472775

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bioRxiv · 10.1101/2021.12.15.472775

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Bunne, C.·Stark, S. G.·Gut, G.·Sarabia del Castillo, J.·Lehmann, K.-V.·Pelkmans, L.·Krause, A.·Ratsch, G.

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Abstract

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, CO_SCPLOWELLC_SCPLOWOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CO_SCPLOWELLC_SCPLOWOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

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Bunne, C., Stark, S. G., Gut, G., Sarabia del Castillo, J., Lehmann, K.-V., Pelkmans, L., Krause, A., Ratsch, G.. 2021-12-15. Learning Single-Cell Perturbation Responses using Neural Optimal Transport. https://doi.org/10.1101/2021.12.15.472775

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