Wrapper function for TrainModel to train a suite of binary classifiers for each cell type present in the data

Usage

TrainModelsFromSeurat(
  seuratObj,
  celltype_column,
  assay = "RNA",
  slot = "data",
  output_dir = "./classifiers",
  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,
  gene_list = NULL,
  gene_exclusion_list = NULL,
  verbose = TRUE,
  min_cells_per_class = 20
)

Arguments

seuratObj: The Seurat Object to be updated
celltype_column: The metadata column containing the celltypes. One classifier will be created for each celltype present in this column.
assay: SeuratObj assay containing the desired count matrix/metadata
slot: Slot containing the count data. Should be restricted to counts, data, or scale.data.
output_dir: The directory in which models, metrics, and training data will be saved.
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
gene_list: If non-null, the input count matrix will be subset to these features
gene_exclusion_list: If non-null, the input count matrix will be subset to drop these features
verbose: Whether or not to print the metrics data for each model after training.
min_cells_per_class: If provided, any classes (and corresponding cells) with fewer than this many cells will be dropped from the training data