  The automated analysis of flow cytometry measurements is an active research
field. We introduce a new algorithm, referred to as CytOpT, using regularized
optimal transport to directly estimate the different cell population
proportions from a biological sample characterized with flow cytometry
measurements. We rely 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). In this work, we rely on a
supervised learning technique based on the Wasserstein metric that is used to
estimate an optimal re-weighting of class proportions in a mixture model from a
source distribution (with known segmentation into cell sub-populations) to fit
a target distribution with unknown segmentation. Due to the high-dimensionality
of flow cytometry data, we use stochastic algorithms to approximate the
regularized Wasserstein metric to solve the optimization problem involved in
the estimation of optimal weights representing the cell population proportions
in the target distribution. Several flow cytometry data sets are used to
illustrate the performances of CytOpT that are also compared to those of
existing algorithms for automatic gating based on supervised learning.
