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Learning Single-Cell Perturbation Responses using Neural Optimal Transport
Charlotte Bunne · Stefan Stark · Gabriele Gut · Andreas Krause · Gunnar Rätsch · Lucas Pelkmans · Kjong Lehmann

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in Optimal Transport and Machine Learning »

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.

Author Information

Charlotte Bunne (ETH Zurich)
Stefan Stark (Department of Computer Science, Swiss Federal Institute of Technology)
Gabriele Gut (University of Zurich)
Andreas Krause (ETH Zurich)
Gunnar Rätsch (ETH Zürich)
Lucas Pelkmans (University of Zurich)
Kjong Lehmann (Swiss Federal Institute of Technology)

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