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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/class.R, R/classifier.R, R/support.R
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\name{checkObjectValidity}
\alias{checkObjectValidity}
\alias{checkCellTypeValidity}
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\alias{checkMarkerGenesValidity}
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\alias{checkParentValidity}
\alias{checkPThresValidity}
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\alias{checkCaretModelValidity}
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\alias{parent<-}
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\alias{parent<-,scAnnotatR-method}
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\alias{caret_model<-}
\alias{caret_model<-,scAnnotatR-method}
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\alias{marker_genes<-}
\alias{marker_genes<-,scAnnotatR-method}
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\alias{train_classifier_seurat}
\alias{train_classifier_sce}
\alias{train_classifier_from_mat}
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\alias{preprocess_seurat_object}
\alias{preprocess_sce_object}
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\alias{test_classifier_seurat}
\alias{test_classifier_sce}
\alias{test_classifier_from_mat}
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\alias{classify_cells_seurat}
\alias{classify_cells_sce}
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\alias{balance_dataset}
\alias{train_func}
\alias{transform_to_zscore}
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\alias{select_marker_genes}
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\alias{check_parent_child_coherence}
\alias{filter_cells}
\alias{construct_tag_vect}
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\alias{process_parent_classifier}
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\alias{make_prediction}
\alias{simplify_prediction}
\alias{verify_parent}
\alias{test_performance}
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\alias{classify_clust}
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\alias{.get_cache}
\alias{download_data_file}
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\title{Internal functions of scAnnotatR package}
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\usage{
checkObjectValidity(object)
checkCellTypeValidity(cell_type)
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checkMarkerGenesValidity(marker_genes)
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checkParentValidity(parent)
checkPThresValidity(p_thres)
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checkCaretModelValidity(caret_model)
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parent(classifier) <- value
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\S4method{parent}{scAnnotatR}(classifier) <- value
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caret_model(classifier) <- value
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\S4method{caret_model}{scAnnotatR}(classifier) <- value
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marker_genes(classifier) <- value
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\S4method{marker_genes}{scAnnotatR}(classifier) <- value
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train_classifier_seurat(
train_obj,
cell_type,
marker_genes,
parent_cell = NA_character_,
parent_classifier = NULL,
path_to_models = "default",
zscore = TRUE,
seurat_tag_slot,
seurat_parent_tag_slot = "predicted_cell_type",
seurat_assay,
seurat_slot
)
train_classifier_sce(
train_obj,
cell_type,
marker_genes,
parent_cell = NA_character_,
parent_classifier = NULL,
path_to_models = "default",
zscore = TRUE,
sce_tag_slot,
sce_parent_tag_slot = "predicted_cell_type",
sce_assay
)
train_classifier_from_mat(
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mat,
tag,
cell_type,
marker_genes,
parent_tag,
parent_cell,
parent_classifier,
path_to_models,
zscore
)
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preprocess_seurat_object(
seurat_obj,
seurat_assay,
seurat_slot,
seurat_tag_slot,
seurat_parent_tag_slot
)
preprocess_sce_object(sce_obj, sce_assay, sce_tag_slot, sce_parent_tag_slot)
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test_classifier_seurat(
test_obj,
classifier,
target_cell_type = NULL,
parent_classifier = NULL,
path_to_models = "default",
zscore = TRUE,
seurat_tag_slot,
seurat_parent_tag_slot = "predicted_cell_type",
seurat_assay,
seurat_slot
)
test_classifier_sce(
test_obj,
classifier,
target_cell_type = NULL,
parent_classifier = NULL,
path_to_models = "default",
zscore = TRUE,
sce_tag_slot,
sce_parent_tag_slot = "predicted_cell_type",
sce_assay
)
test_classifier_from_mat(
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mat,
tag,
classifier,
parent_tag,
target_cell_type,
parent_classifier,
path_to_models,
zscore
)
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classify_cells_seurat(
classify_obj,
classifiers = NULL,
cell_types = "all",
chunk_size = 5000,
path_to_models = "default",
ignore_ambiguous_result = FALSE,
cluster_slot,
seurat_assay,
seurat_slot
)
classify_cells_sce(
classify_obj,
classifiers = NULL,
cell_types = "all",
chunk_size = 5000,
path_to_models = "default",
ignore_ambiguous_result = FALSE,
sce_assay,
cluster_slot = NULL
)
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balance_dataset(mat, tag)
train_func(mat, tag)
transform_to_zscore(mat)
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select_marker_genes(mat, marker_genes)
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check_parent_child_coherence(
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mat,
tag,
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pos_parent,
parent_cell,
cell_type,
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target_cell_type
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)
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filter_cells(mat, tag)
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construct_tag_vect(tag, cell_type)
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process_parent_classifier(
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mat,
parent_tag,
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parent_cell_type,
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parent_classifier,
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path_to_models,
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zscore
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)
make_prediction(mat, classifier, pred_cells, ignore_ambiguous_result = TRUE)
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simplify_prediction(meta.data, full_pred, classifiers)
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verify_parent(mat, classifier, meta.data)
test_performance(mat, classifier, tag)
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classify_clust(clusts, most_probable_cell_type)
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.get_cache()
download_data_file(verbose = FALSE)
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}
\arguments{
\item{object}{The request classifier to check.}
\item{cell_type}{name of cell type}
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\item{marker_genes}{list of selected marker genes}
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\item{parent}{Classifier parent to check.}
\item{p_thres}{Classifier probability threshold to check.}
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\item{caret_model}{Classifier to check.}
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\item{classifier}{classifier}
\item{value}{the new classifier}
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\item{train_obj}{SCE object}
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\item{parent_cell}{name of parent cell type}
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\item{parent_classifier}{\code{\link{scAnnotatR}} object corresponding
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to classification model for the parent cell type}
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\item{path_to_models}{path to databases, or by default}
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\item{zscore}{boolean indicating the transformation of gene expression
in object to zscore or not}
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\item{seurat_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{seurat_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.}
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\item{seurat_assay}{name of assay to use in Seurat object}
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\item{seurat_slot}{type of expression data to use in Seurat object.
Some available types are: "counts", "data" and "scale.data".}
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\item{sce_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{sce_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.}
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\item{sce_assay}{name of assay to use in SCE object}
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\item{mat}{expression matrix}
\item{tag}{tag of data}
\item{parent_tag}{vector, named list indicating pre-assigned/predicted
parent cell type}
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\item{seurat_obj}{Seurat object}
\item{sce_obj}{Seurat object}
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\item{test_obj}{SCE object used for testing}
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\item{target_cell_type}{alternative cell types (in case of testing classifier)}
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\item{classify_obj}{the SCE object containing cells to be classified}
\item{classifiers}{classifiers}
\item{cell_types}{list of cell types containing models to be used
for classification, only applicable if the models have been saved to package.}
\item{chunk_size}{size of data chunks to be predicted separately.
This option is recommended for large datasets to reduce running time.
Default value at 5000, because smaller datasets can be predicted rapidly.}
\item{ignore_ambiguous_result}{whether ignore ambigouous result}
\item{cluster_slot}{name of slot in meta data containing cluster
information, in case users want to have additional cluster-level
prediction}
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\item{pos_parent}{a vector indicating parent classifier prediction}
\item{parent_cell_type}{name of parent cell type}
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\item{pred_cells}{a whole prediction for all cells}
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\item{meta.data}{object meta data}
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\item{full_pred}{full prediction}
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\item{clusts}{cluster info}
\item{most_probable_cell_type}{predicted cell type}
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\item{verbose}{logical indicating downloading the file or not}
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}
\value{
TRUE if the classifier is valid or the reason why it is not
TRUE if the cell type is valid or the reason why it is not.
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TRUE if the marker_genes is valid or the reason why it is not.
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TRUE if the parent is valid or the reason why it is not.
TRUE if the p_thres is valid or the reason why it is not.
TRUE if the classifier is valid or the reason why it is not.
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the classifier with the new parent.
scAnnotatR object with the new parent
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the classifier with the new core caret model.
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scAnnotatR object with the new trained classifier.
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the classifier with the new marker genes
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scAnnotatR object with the new marker genes.
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\code{\link{scAnnotatR}} object
\code{\link{scAnnotatR}} object
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caret trained model
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a list containing: expression matrix of size n x m, n: genes, m: cells;
a vector indicating cell type, and a vector containing parent cell type.
a list containing: expression matrix of size n x m, n: genes, m: cells;
a vector indicating cell type, and a vector containing parent cell type.
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result of testing process in form of a list,
including predicted values, prediction accuracy at a probability threshold,
and roc curve information.
result of testing process in form of a list,
including predicted values, prediction accuracy at a probability threshold,
and roc curve information.
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model performance statistics
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the input object with new slots in cells meta data
New slots are: predicted_cell_type, most_probable_cell_type,
slots in form of [cell_type]_p, [cell_type]_class, and clust_pred
(if cluster_slot was provided).
the input object with new slots in cells meta data
New slots are: predicted_cell_type, most_probable_cell_type,
slots in form of [cell_type]_p, [cell_type]_class, and clust_pred
(if cluster_slot was provided).
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a list of balanced count matrix
and corresponding tags of balanced count matrix
the classification model (caret object)
row wise center-scaled count matrix
filtered matrix
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list of adjusted tag
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filtered matrix and corresponding tag
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a binary vector for cell tag
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list of cells which are positive to parent classifier
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prediction
simplified prediction
applicable matrix
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classifier performance
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BiocFileCache object
path to the downloaded file in cache
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}
\description{
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Check if a scAnnotatR object is valid
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Train a classifier for a new cell type
If cell type has a parent, only available for \code{\link{scAnnotatR}}
object as parent cell classifying model.
Train a classifier for a new cell type
If cell type has a parent, only available for \code{\link{scAnnotatR}}
object as parent cell classifying model.
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Train a classifier for a new cell type from expression matrix
and tag
If cell type has a parent, only available for \code{\link{scAnnotatR}}
object as parent cell classifying model.
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Preprocess Seurat object to produce expression matrix,
tag, parent cell tag.
Preprocess Seurat object to produce expression matrix,
tag, parent cell tag.
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Testing process when test object is of type Seurat
Testing process when test object is of type SCE
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Testing process from matrix and tag
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}
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