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.

@article{bunne2021learning,
	author = {Charlotte Bunne and Stefan G. Stark and Gabriele Gut and Jacobo Sarabia del Castillo and Mitchell Levesque and Kjong-Van Lehmann and Lucas Pelkmans and Andreas Krause and Gunnar Ratsch},
	journal = {Nature Methods},
	month = {August},
	publisher = {Nature Publishing Group},
	title = {Learning Single-Cell Perturbation Responses using Neural Optimal Transport},
	year = {2023}}