Creates a binary classifier to classify cells within a Seurat object

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