Label_Prop_sto_r {CytOpT}

Computes a classification on the target data

Description

Computes a classification on the target data thanks to the approximation of the transport plan and the classification of the source data. Transport plan is approximated with the stochastic algorithm.

Usage

Label_Prop_sto_r(
  X_s,
  X_t,
  Lab_source,
  eps = 1e-04,
  const = 0.1,
  n_iter = 4000,
  minMaxScaler = TRUE,
  monitoring = TRUE,
  thresholding = TRUE
)

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

eps

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

const

an float constant. Default is 1e-01

n_iter

an integer Constant that iterate method select. Default is 4000

minMaxScaler

a logical flag indicating to possibly Scaler

monitoring

a logical flag indicating to possibly monitor the gap between the estimated proportions and the manual gold-standard. Default is FALSE

thresholding

a logical flag.

Value

a ggplot object

a vector of length nrow(X_t) with the propagated labels

Examples

if(interactive()){

res <- Label_Prop_sto_r(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels)

}