TrainModel: Creates a binary classifier to classify cells within a Seurat...

In bimberlabinternal/RIRA_classification: A package for the Rhesus Immune Reference Atlas (RIRA): A multi-tissue single-cell landscape of immune cells

Creates a binary classifier to classify cells within a Seurat object

Description

Creates a binary classifier to classify cells

Usage

TrainModel(
  training_matrix,
  celltype,
  hyperparameter_tuning = F,
  learner = "classif.ranger",
  inner_resampling = "cv",
  outer_resampling = "cv",
  inner_folds = 4,
  inner_ratio = 0.8,
  outer_folds = 3,
  outer_ratio = 0.8,
  n_models = 20,
  n_cores = NULL
)

Arguments

training_matrix	A counts or data slot provided by TrainModelsFromSeurat
celltype	The celltype (provided by TrainModelsFromSeurat) used as classifier's positive prediction
hyperparameter_tuning	logical that determines whether or not hyperparameter tuning should be performed.
learner	The mlr3 learner that should be used. Currently fixed to "classif.ranger" if hyperparameter tuning is FALSE. Otherwise, "classif.xgboost" and "classif.ranger" are supported.
inner_resampling	The resampling strategy that is used for hyperparameter optimization. Holdout ("hout" or "holdout") and cross validation ("cv" or "cross-validation") are supported.
outer_resampling	The resampling strategy that is used to determine overfitting. Holdout ("hout" or "holdout") and cross validation ("cv" or "cross-validation") are supported.
inner_folds	The number of folds to be used for inner_resampling if cross-valdiation is performed.
inner_ratio	The ratio of training to testing data to be used for inner_resampling if holdout resampling is performed.
outer_folds	The number of folds to be used for outer_resampling if cross-valdiation is performed.
outer_ratio	The ratio of training to testing data to be used for inner_resampling if holdout resampling is performed.
n_models	The number of models to be trained during hyperparameter tuning. The model with the highest accuracy will be selected and returned.
n_cores	If non-null, this number of workers will be used with future::plan

bimberlabinternal/RIRA_classification documentation built on April 14, 2025, 5:59 p.m.