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 (2023)