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) <doi:10.1214/22-AOAS1660>.
Version: 0.9.8 Depends: R (≥ 3.6) Imports: ggplot2 (≥ 3.0.0), MetBrewer, patchwork, reshape2, reticulate, stats, testthat (≥ 3.0.0) Suggests: rmarkdown, knitr, covr Published: 2025-04-01 DOI: 10.32614/CRAN.package.CytOpT Author: Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl] Maintainer: Boris Hejblum <boris.hejblum at u-bordeaux.fr> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://sistm.github.io/CytOpT-R/, https://github.com/sistm/CytOpT-R/ NeedsCompilation: no SystemRequirements: Python (>= 3.7) Language: en-US Citation: CytOpT citation info Materials: README NEWS CRAN checks: CytOpT results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=CytOpT to link to this page.
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