Given a trained model (or list of models) and a rank-normalized expression matrix, predicts viability scores for each cell/sample across one or more drugs. This function merges the former viability_from_model (single drug) and viability_in_each_dataset (multi-drug) into a unified interface.
Arguments
- model_list
A named list of model objects (each with a
$modelelement), or a single model object. Fromtrain_models()orload_model().- expr
Matrix or data frame. Rank-normalized expression matrix with genes as rows and cells/samples as columns.
Value
A matrix with cells/samples as rows and drugs as columns, containing predicted viability scores. Lower values indicate higher drug sensitivity.
Details
If a small fraction (<= 50\
expr (e.g. genes filtered out during scRNA QC), they are imputed
with the neutral rank value 0.5 and a warning is issued. If more than half
of the features are missing, prediction stops with an error.
Examples
if (FALSE) { # \dontrun{
# Single drug
models <- load_model("erlotinib", read = TRUE)
pred <- predict_drugs(models, expr_rnorm)
# Multiple drugs
models <- train_models(drug_list = c("abemaciclib", "erlotinib"),
cancer_type = "PanCan", exclude_cancer = "PanCan", GOI = GOI)
pred <- predict_drugs(models, expr_rnorm)
} # }