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CytOpT  

Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation
View on CRAN: Click here


Download and install CytOpT package within the R console
Install from CRAN:
install.packages("CytOpT")

Install from Github:
library("remotes")
install_github("cran/CytOpT")

Install by package version:
library("remotes")
install_version("CytOpT", "0.9.4")



Attach the package and use:
library("CytOpT")
Maintained by
Boris Hejblum
[Scholar Profile | Author Map]
First Published: 2022-02-07
Latest Update: 2022-02-09
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) .
How to cite:
Boris Hejblum (2022). CytOpT: Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation. R package version 0.9.4, https://cran.r-project.org/web/packages/CytOpT. Accessed 10 Apr. 2025.
Previous versions and publish date:
0.9.2 (2022-02-07 10:30), 0.9.4 (2022-02-09 18:10), 0.9.6 (2025-03-26 11:00), 0.9.7 (2025-03-30 19:00)
Other packages that cited CytOpT R package
View CytOpT citation profile
Other R packages that CytOpT depends, imports, suggests or enhances
Complete documentation for CytOpT
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