cytopt_minmax_r {CytOpT} R Documentation

## 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.

### Description

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.

### Usage

cytopt_minmax_r(
X_s,
X_t,
Lab_source,
theta_true = NULL,
eps = 1e-04,
lbd = 1e-04,
n_iter = 10000,
step = 5,
power = 0.99,
monitoring = FALSE
)


### Arguments

 X_s Cytometry data set. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The classification of this Cytometry data set must be provided with the Lab_source parameters. X_t Cytometry data set. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The CytOpT algorithm targets the cell type proportion in this Cytometry data set. Lab_source Classification of the X_s Cytometry data set theta_true If available, gold-standard proportions in the target data set X_t derived from manual gating. It allows to assess the gap between the estimate and the gold-standard. Default is NULL, in which case no assessment is performed. eps Regularization parameter of the Wasserstein distance lbd an float constant that multiply the step-size policy. Default is 1e-04. n_iter an integer Constant that iterate method select. Default is 10000. step Constant that multiply the step-size policy. Default is 5. power the step size policy of the gradient ascent method is step/n^power. Default is 0.99. monitoring boolean indicating whether Kullback-Leibler divergence should be monitored and store throughout the optimization iterations. Default is FALSE.

### Value

A list with the following elements:Results_Minmax

[Package CytOpT version 0.9.4 Index]