CytOpT: Optimal Transport for Gating Transfer in Cytometry Data withDomain 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 (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 |
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