TrainModelsFromSeurat: Wrapper function for TrainModel to train a suite of binary...

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

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

Description

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

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