CytOpT {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 the computation of the estimate of the class proportions is done with a descent ascent or minmax or two algorithms.
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 the computation of the estimate of the class proportions is done with a descent ascent or minmax or two algorithms.
Usage
CytOpT(
X_s,
X_t,
Lab_source,
Lab_target = NULL,
theta_true = NULL,
method = c("minmax", "desasc", "both"),
eps = 1e04,
n_iter = 10000,
power = 0.99,
step_grad = 10,
step = 5,
lbd = 1e04,
n_out = 5000,
n_stoc = 10,
minMaxScaler = TRUE,
monitoring = FALSE,
thresholding = TRUE
)
Arguments
X_s 
a cytometry dataframe with only 
X_t 
a cytometry dataframe with only 
Lab_source 
a vector of length 
Lab_target 
a vector of length 
theta_true 
If available, goldstandard proportions in the target data
set 
method 
a character string indicating which method to use to
compute the cytopt, either 
eps 
a float value of regularization parameter of the Wasserstein distance. Default is 
n_iter 
an integer Constant that iterate method select. Default is 
power 
a float constant the step size policy of the gradient ascent method is step/n^power. Default is 
step_grad 
an integer number step size of the gradient descent algorithm of the outer loop.
Default is 
step 
an integer constant that multiply the stepsize policy. Default is 
lbd 
a float constant that multiply the stepsize policy. Default is 
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 
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 
minMaxScaler 
a logical flag indicating to whether to scale observations
between 0 and 1. Default is 
monitoring 
a logical flag indicating to possibly monitor the gap between the estimated proportions and the manual
goldstandard. Default is 
thresholding 
a logical flag indicating whether to threshold negative
values. Default is 
Value
a object of class CytOpt
, which is a list of two elements:

proportions
adata.frame
with the (optionally true and) estimated proportions for eachmethod

monitoring
a list of estimates over the optimization iterations for eachmethod
(listed within)
Examples
if(interactive()){
res < CytOpT(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A,
Lab_source = HIPC_Stanford_1228_1A_labels,
method='minmax')
summary(res)
plot(res)
}