Skip to contents

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.

Source: R/cytopt_desasc.R
cytopt_desasc_r.Rd

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.

Usage

cytopt_desasc_r(
  X_s,
  X_t,
  Lab_source,
  theta_true = NULL,
  eps = 1e-04,
  n_out = 5000,
  n_stoc = 10,
  step_grad = 10,
  monitoring = FALSE
)

Arguments

X_s

a cytometry dataframe. 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

a cytometry dataframe. 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

a vector of length n 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

an float value of regularization parameter of the Wasserstein distance. Default is 1e-04.

n_out

an integer number of iterations in the outer loop. This loop corresponds to the gradient descent algorithm to minimize the regularized Wasserstein distance between the source and target data sets. Default is 5000.

n_stoc

an integer number of iterations in the inner loop. This loop corresponds to the stochastic algorithm that approximates a maximizer of the semi-dual problem. Default is 10.

step_grad

an integer number step size of the gradient descent algorithm of the outer loop. Default is 10.

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:h_hat