Optimal Transport for Gating Transfer in Cytometry Data with
Domain Adaptation // Supervised learning from a source
distribution (with known segmentation into cell sub-
populations) to fit a target distribution with unknown
segmentation. It relies regularized optimal transport to
directly estimate the different cell population proportions
from a biological sample characterized with flow cytometry
measurements. It is based on the regularized Wasserstein metric
to compare cytometry measurements from different samples, thus
accounting for possible mis-alignment of a given cell
population across sample (due to technical variability from the
technology of measurements). Supervised learning technique
based on the Wasserstein metric that is used to estimate an
optimal re-weighting of class proportions in a mixture model
Details are presented in Freulon P, Bigot J and Hejblum BP
(2021) arXiv:2006.09003.