% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/classifier.R
\name{test_classifier}
\alias{test_classifier}
\alias{test_classifier,scAnnotatR-method}
\title{Testing process.}
\usage{
test_classifier(
  classifier,
  test_obj,
  assay,
  slot = NULL,
  tag_slot,
  target_cell_type = NULL,
  parent_classifier = NULL,
  parent_tag_slot = "predicted_cell_type",
  path_to_models = "default",
  zscore = TRUE
)

\S4method{test_classifier}{scAnnotatR}(
  classifier,
  test_obj,
  assay,
  slot = NULL,
  tag_slot,
  target_cell_type = NULL,
  parent_classifier = NULL,
  parent_tag_slot = "predicted_cell_type",
  path_to_models = "default",
  zscore = TRUE
)
}
\arguments{
\item{classifier}{scAnnotatR classification model}

\item{test_obj}{object that can be used for testing}

\item{assay}{name of assay to use in test_object}

\item{slot}{type of expression data to use in test_object. 
For Seurat object, some available types are: "counts", "data" and "scale.data".
Ignore this if test_obj is \code{\link{SingleCellExperiment}} object.}

\item{tag_slot}{string, name of annotation slot 
indicating cell tag/label in the testing object.
Strings indicating cell types are expected in this slot. 
Expected values are string (A-Z, a-z, 0-9, no special character accepted) 
or binary/logical, 0/"no"/F/FALSE: not being new cell type, 
1/"yes"/T/TRUE: being new cell type.}

\item{target_cell_type}{vector indicating other cell types than cell labels 
that can be considered as the main cell type in classifier, 
for example, c("plasma cell", "b cell", "b cells", "activating b cell"). 
Default as NULL.}

\item{parent_classifier}{\code{\link{scAnnotatR}} object
corresponding to classification model for the parent cell type}

\item{parent_tag_slot}{string, name of tag slot in cell meta data
indicating pre-assigned/predicted parent cell type. 
Default field is "predicted_cell_type".
The slot must contain only string values.}

\item{path_to_models}{path to the folder containing the list of models. 
As default, the pretrained models in the package will be used. 
If user has trained new models, indicate the folder containing 
the new_models.rda file.}

\item{zscore}{boolean, whether gene expression is transformed to zscore}
}
\value{
result of testing process in form of a list, 
including predicted values, prediction accuracy at a probability threshold, 
and roc curve information.
}
\description{
Testing process.
}
\note{
Only one cell type is expected for each cell. 
Ambiguous cell type, such as: "T cells/NK cells/ILC", will be ignored.
Subtypes used in testing model for parent cell types can be indicated 
as parent cell type, or can be indicated in target_cell_type. 
For example, when testing for B cells, plasma cells can be annotated as 
B cells, or target_cell_type is set c("plasma cells").
}
\examples{
# load small example dataset
data("tirosh_mel80_example")

# train the classifier
selected_marker_genes_B = c("CD19", "MS4A1", "CD79A")
set.seed(123)
classifier_b <- train_classifier(train_obj = tirosh_mel80_example,
assay = 'RNA', slot = 'counts', marker_genes = selected_marker_genes_B, 
cell_type = "b cells", tag_slot = 'active.ident')

# test the classifier, target cell type can be in other formats or
# alternative cell type that can be considered as the classified cell type 
classifier_b_test <- test_classifier(classifier = classifier_b, 
test_obj = tirosh_mel80_example, assay = 'RNA', slot = 'counts', 
tag_slot = 'active.ident', target_cell_type = c("B cell"))
classifier_b_test

}