CRAN/E | CytOpT

CytOpT

Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation

Installation

About

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) .

Citation CytOpT citation info
sistm.github.io/CytOpT-R/
github.com/sistm/CytOpT-R/
System requirements Python (>= 3.7)

Key Metrics

Version 0.9.4
R ≥ 3.6
Published 2022-02-09 801 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks CytOpT results
Language en-US

Downloads

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Maintainer

Maintainer

Boris Hejblum

boris.hejblum@u-bordeaux.fr

Authors

Boris Hejblum

aut / cre

Paul Freulon

aut

Kalidou Ba

aut / trl

Material

README
NEWS
Reference manual
Package source

Vignettes

User guide for executing 'CytOpT' on 'HIPC' data

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

CytOpT archive

Depends

R ≥ 3.6

Imports

ggplot2 ≥ 3.0.0
MetBrewer
patchwork
reshape2
reticulate
stats
testthat ≥ 3.0.0

Suggests

rmarkdown
knitr
covr