Package: CytOpT 0.9.4

Boris Hejblum

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) <arxiv:2006.09003>.

Authors:Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl]

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CytOpT.pdf |CytOpT.html✨
CytOpT/json (API)
NEWS

# Install 'CytOpT' in R:
install.packages('CytOpT', repos = c('https://sistm.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sistm/cytopt-r/issues

Datasets:

On CRAN:

7 exports 1 stars 0.84 score 58 dependencies 5 scripts 212 downloads

Last updated 3 years agofrom:0e45463a55. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 16 2024
R-4.5-winOKSep 16 2024
R-4.5-linuxOKSep 16 2024
R-4.4-winOKSep 16 2024
R-4.4-macOKSep 16 2024
R-4.3-winOKSep 16 2024
R-4.3-macOKSep 16 2024

Exports:barplot_propBland_AltmanCytOpTcytopt_desasc_rcytopt_minmax_rKL_plotLabel_Prop_sto_r

Dependencies:briocallrclicolorspacecrayondescdiffobjdigestevaluatefansifarverfsggplot2gluegtablehereisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixMetBrewermgcvmunsellnlmepatchworkpillarpkgbuildpkgconfigpkgloadplyrpngpraiseprocessxpsR6rappdirsRColorBrewerRcppRcppTOMLrematch2reshape2reticulaterlangrprojrootscalesstringistringrtestthattibbleutf8vctrsviridisLitewaldowithr

User guide for executing CytOpT on HIPC data

Paul Freulon, Jérémie Bigot, Kalidou BA, Boris Hejblum

Rendered fromCytOpt_HIPC.Rmdusingknitr::rmarkdownon Sep 16 2024.

Last update: 2022-02-07
Started: 2022-01-24

Readme and manuals

Help Manual

Help pageTopics
Function to display a bland plot in order to visually assess the agreement between CytOpt estimation of the class proportions and the estimate of the class proportions provided through manual gating.barplot_prop
Bland & Altman plotBland_Altman
Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function the computation of the estimate of the class proportions is done with a descent ascent or minmax or two algorithms.CytOpT
Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function the computation of the estimate of the class proportions is done with a descent ascent algorithm.cytopt_desasc_r
Function to estimate the type cell proportions in an unclassified cytometry data set denoted X_s by using the classification Lab_source from an other cytometry data set X_s. With this function an additional regularization parameter on the class proportions enables a faster computation of the estimator.cytopt_minmax_r
HIPC_Stanford dataHIPC_Stanford HIPC_Stanford_1228_1A HIPC_Stanford_1228_1A_labels HIPC_Stanford_1369_1A HIPC_Stanford_1369_1A_labels
Kullback-Leibler divergence plotKL_plot
Computes a classification on the target dataLabel_Prop_sto_r
CytOpt plotplot.CytOpt
CytOpt printprint.CytOpt
CytOpt print summaryprint.summary.CytOpt
CytOpt summarysummary.CytOpt