CytOpT: Optimal Transport for Gating Transfer in Cytometry Data with
Domain 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 (2021) <doi:10.48550/arXiv.2006.09003>.
| Version: |
0.9.4 |
| Depends: |
R (≥ 3.6) |
| Imports: |
ggplot2 (≥ 3.0.0), MetBrewer, patchwork, reshape2, reticulate, stats, testthat (≥ 3.0.0) |
| Suggests: |
rmarkdown, knitr, covr |
| Published: |
2022-02-09 |
| 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:
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