internal: Internal functions of scClassifR package

In grisslab/scClassifR: Pretrained learning models for cell type prediction on single cell RNA-sequencing data

Description

Check if a scClassifR object is valid

Filter cells from ambiguous chars and non applicable cells in a Seurat object

Filter cells from ambiguous chars and non applicable cells in a SingleCellExperiment object

Construct a uniform tag vector for all forms of labels in a Seurat object

Construct a uniform tag vector for all forms of labels in a SingleCellExperiment object

Process parent classifier in a Seurat object

Process parent classifier in a SingleCellExperiment object

Usage

checkObjectValidity(object)

checkCellTypeValidity(cell_type)

checkFeaturesValidity(features)

checkParentValidity(parent)

checkPThresValidity(p_thres)

checkClassifierValidity(clf)

parent(classifier) <- value

clf(classifier) <- value

features(classifier) <- value

balance_dataset(mat, tag)

train_func(mat, tag)

transform_to_zscore(mat)

load_models(path_to_models)

select_features(mat, features)

check_parent_child_coherence(
  obj,
  pos_parent,
  parent_cell,
  cell_type,
  target_cell_type,
  ...
)

filter_cells(obj, tag_slot)

## S4 method for signature 'Seurat'
filter_cells(obj, tag_slot)

## S4 method for signature 'SingleCellExperiment'
filter_cells(obj, tag_slot)

construct_tag_vect(obj, cell_type, ...)

## S4 method for signature 'Seurat'
construct_tag_vect(obj, cell_type, tag_slot)

## S4 method for signature 'SingleCellExperiment'
construct_tag_vect(obj, cell_type, tag_slot)

process_parent_clf(
  obj,
  parent_tag_slot,
  parent_cell_type,
  parent_clf,
  path_to_models,
  zscore = TRUE,
  ...
)

## S4 method for signature 'Seurat'
process_parent_clf(
  obj,
  parent_tag_slot,
  parent_cell_type,
  parent_clf,
  path_to_models,
  zscore = TRUE,
  seurat_assay,
  seurat_slot,
  ...
)

## S4 method for signature 'SingleCellExperiment'
process_parent_clf(
  obj,
  parent_tag_slot,
  parent_cell_type,
  parent_clf,
  path_to_models,
  zscore = TRUE,
  sce_assay,
  ...
)

make_prediction(mat, classifier, pred_cells, ignore_ambiguous_result = TRUE)

simplify_prediction(meta.data, full_pred, classifiers)

verify_parent(mat, classifier, meta.data)

test_performance(mat, classifier, tag)

classify_clust(clusts, most_probable_cell_type)

Arguments

object	The request classifier to check.
cell_type	name of cell type
features	list of selected features
parent	Classifier parent to check.
p_thres	Classifier probability threshold to check.
clf	Classifier to check.
classifier	classifier
value	the new classifier
mat	expression matrix
tag	tag of data
path_to_models	path to databases, or by default
obj	object
pos_parent	a vector indicating parent clf prediction
parent_cell	name of parent cell type
target_cell_type	alternative cell types (in case of testing clf)
...	arguments passed to other methods
tag_slot	tag slot in SingleCellExperiment object indicating cell type
parent_tag_slot	string, name of annotation tag slot in object indicating pre-assigned/predicted parent cell type
parent_cell_type	name of parent cell type
parent_clf	scClassifR object corresponding to classification model for the parent cell type
zscore	boolean indicating the transformation of gene expression in object to zscore or not
seurat_assay	name of assay to use in Seurat object
seurat_slot	type of expression data to use in Seurat object
sce_assay	name of assay to use in SingleCellExperiment object
pred_cells	a whole prediction for all cells
ignore_ambiguous_result	whether ignore ambigouous result
meta.data	object meta data
full_pred	full prediction
classifiers	classifiers
clusts	cluster info
most_probable_cell_type	predicted cell type

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.

TRUE if the features is valid or the reason why it is not.

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.

scClassifR object with the new parent.

scClassifR object with the new trained classifier.

scClassifR object with the new features.

a list of balanced count matrix and corresponding tags of balanced count matrix

the classification model (caret object)

row wise center-scaled count matrix

list of classifiers

filtered matrix

list of adjusted object and adjusted tag slot

adjusted Seurat object

adjusted SingleCellExperiment object

a binary vector for cell tag

list of cells which are positive to parent clf

prediction

simplified prediction

applicable matrix

clf performance

grisslab/scClassifR documentation built on Oct. 27, 2021, 12:13 p.m.