save_new_model: Save a model to the package

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

Save a model to the package

Description

Save a model to the package

Usage

save_new_model(new_model, include.default = TRUE, path_to_models = tempdir())

Arguments

new_model	new model to be added into the classification tree
include.default	whether include the default models of the package in the list of new trained models or not. If users further want to classify cells, they can only use default pretrained model list or their new model list. Including default models in new trained models helps users using both of them once. In addition, default pretrained models of the package cannot be changed or removed. This can be done with the new trained model list.
path_to_models	path to the folder containing the list of new models.

Value

no return value, but the model is now saved to database

Examples

# load small example dataset
data("tirosh_mel80_example")

# train classifier
selected_marker_genes_T = c("CD4", "CD8A", "CD8B")
set.seed(123)
classifier_t <- train_classifier(train_obj = tirosh_mel80_example,
assay = 'RNA', slot = 'counts', marker_genes = selected_marker_genes_T, 
cell_type = "t cells", tag_slot = 'active.ident')

# save the trained classifier to system 
# test classifier can be used before this step
# Note: We do not include the default models here to runtime of the example
save_new_model(new_model = classifier_t, path_to_models = tempdir(), 
               include.default = FALSE)

# verify if new model has been saved
print(names(load(file.path(tempdir(), "new_models.rda"))))
delete_model("t cells")

grisslab/scAnnotatR documentation built on March 20, 2023, 2:42 a.m.