print.summary.CytOpt

print S3 method for summary.CytOpt object

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 (2023) <doi:10.1214/22-AOAS1660>.

Boris P. Hejblum

CytOpT

Optimal Transport for Gating Transfer in Cytometry Data with
Domain Adaptation

Boris Hejblum

Paul Freulon

Kalidou Ba

print.summary.CytOpt function

<dl><dt>x</dt>
<dd>an object of class <code>summary.CytOpt</code> to print.</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods. Not implemented.</dd></dl>

Arguments

CytOpt print summary — print.summary.CytOpt

<dl>

<dt>x</dt>
<dd>an object of class <code>summary.CytOpt</code> to print.</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods. Not implemented.</dd>

</dl>

print.summary.CytOpt: CytOpt print summary

Description

Usage

Arguments