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) <doi:10.48550/arXiv.2006.09003>.

Version: 0.9.4
Depends: R (≥ 3.6)
Imports: ggplot2 (≥ 3.0.0), MetBrewer, patchwork, reshape2, reticulate, stats, testthat (≥ 3.0.0)
Suggests: rmarkdown, knitr, covr
Published: 2022-02-09
DOI: 10.32614/CRAN.package.CytOpT
Author: Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl]
Maintainer: Boris Hejblum <boris.hejblum at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
SystemRequirements: Python (>= 3.7)
Language: en-US
Citation: CytOpT citation info
Materials: README NEWS
CRAN checks: CytOpT results


Reference manual: CytOpT.pdf
Vignettes: User guide for executing 'CytOpT' on 'HIPC' data


Package source: CytOpT_0.9.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CytOpT_0.9.4.tgz, r-oldrel (arm64): CytOpT_0.9.4.tgz, r-release (x86_64): CytOpT_0.9.4.tgz, r-oldrel (x86_64): CytOpT_0.9.4.tgz
Old sources: CytOpT archive


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