Package: CytOpT
Type: Package
Title: Optimal Transport for Gating Transfer in Cytometry Data with
        Domain Adaptation
Version: 0.9.4
Date: 2022-02-08
Authors@R: c(person("Boris", "Hejblum", role = c("aut", "cre"), email = "boris.hejblum@u-bordeaux.fr"),
              person("Paul", "Freulon", role = c("aut"), email = "paul.freulon@math.u-bordeaux.fr"),
              person("Kalidou", "Ba", role = c("aut", "trl")))
Maintainer: Boris Hejblum <boris.hejblum@u-bordeaux.fr>
SystemRequirements: Python (>= 3.7)
Description: 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>.
Config/reticulate: list( packages = list( list(package = "numpy"),
        list(package = "scikit-learn"), list(package = "scipy") ) )
License: GPL (>= 2)
Repository: CRAN
URL: https://sistm.github.io/CytOpT-R/,
        https://github.com/sistm/CytOpT-R/
Depends: R (>= 3.6)
LazyData: true
RoxygenNote: 7.1.2
Encoding: UTF-8
Imports: ggplot2 (>= 3.0.0), MetBrewer, patchwork, reshape2,
        reticulate, stats, testthat (>= 3.0.0)
Suggests: rmarkdown, knitr, covr
Config/testthat/edition: 3
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2022-02-09 16:32:16 UTC; boris
Author: Boris Hejblum [aut, cre],
  Paul Freulon [aut],
  Kalidou Ba [aut, trl]
Date/Publication: 2022-02-09 17:10:06 UTC
