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DESCRIPTION
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DESCRIPTION
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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