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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 |
| 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 |
| Reference manual: | CytOpT.pdf |
| Vignettes: |
User guide for executing 'CytOpT' on 'HIPC' data |
| Package source: | CytOpT_0.9.4.tar.gz |
| Windows binaries: | r-devel: CytOpT_0.9.4.zip, r-release: CytOpT_0.9.4.zip, r-oldrel: CytOpT_0.9.4.zip |
| macOS binaries: | r-release (arm64): CytOpT_0.9.4.tgz, r-oldrel (arm64): CytOpT_0.9.4.tgz, r-release (x86_64): CytOpT_0.9.4.tgz, r-oldrel (x86_64): CytOpT_0.9.4.tgz |
| Old sources: | CytOpT archive |
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