Package: CytOpT Type: Package Title: Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation Version: 0.9.8 Date: 2025-03-31 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 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 (2023) . License: GPL (>= 2) URL: https://sistm.github.io/CytOpT-R/, https://github.com/sistm/CytOpT-R/ Depends: R (>= 3.6) LazyData: true RoxygenNote: 7.3.2 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 Config/pak/sysreqs: cmake make libicu-dev libpng-dev libuv1-dev python3 Repository: https://sistm.r-universe.dev Date/Publication: 2025-03-31 22:03:28 UTC RemoteUrl: https://github.com/sistm/cytopt-r RemoteRef: HEAD RemoteSha: 3981460b3acd799ce196d454999c9c7057f69206 NeedsCompilation: no Packaged: 2026-06-11 08:47:39 UTC; root Author: Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl]