Plot validation ROC curves for trained models — plot_model

For each model and each validation dataset (bulk, pseudo-bulk, single-cell), the observed response is stratified into the top vs bottom 50\ (resistant vs sensitive – the PERCEPTION paper's convention, Extended Data Fig. 4C) and a ROC curve of the predicted viability is drawn, one curve per dataset, with the AUC annotated in the legend. Higher AUC = the model stratifies better. This is the most informative single-model summary after training.

Usage

plot_model_roc(performance_list, base_size = 13)

Arguments

performance_list: Named list of model objects from train_models(), each with a $predVSgroundTruth element.
base_size: Numeric. Base font size. Default = 13.

Value

A ggplot object: ROC curves per validation dataset, faceted by drug when more than one model is provided.

Examples

if (FALSE) { # \dontrun{
  models <- train_models(drug_list = "abemaciclib", ...)
  plot_model_roc(models)
} # }