CytOpT: 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>.

Package details

AuthorBoris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl]
MaintainerBoris Hejblum <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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CytOpT documentation built on Feb. 10, 2022, 1:07 a.m.