Supervised learning from a source distribution (with known segmentation into cell subpopulations) 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 misalignment 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 reweighting of class proportions in a mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2021) <arXiv:2006.09003>.
Package details 


Author  Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl] 
Maintainer  Boris Hejblum <boris.hejblum@ubordeaux.fr> 
License  GPL (>= 2) 
Version  0.9.4 
URL  https://sistm.github.io/CytOpTR/ https://github.com/sistm/CytOpTR/ 
Package repository  View on CRAN 
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