Core training function that builds a glmnet model using bulk expression, then evaluates on pseudo-bulk, bulk test, and single-cell test data. This is the direct port of build_on_BULK_v2 from step0B.
Usage
build_on_BULK_v2(
infunc_drugName,
infunc_cancerType = "PanCan",
exclude_cancer = "PanCan",
infunc_features,
single_best = infunc_features[1],
k_features = 100,
infunc_alpha = 1,
model_type = "glmnet",
num_folds = 3,
num_tree = 500,
seed = 1,
alpha_gradient = 0.05,
lambda_gradient = 20,
lambda_range = c(1e-04, 1),
cv_method = "cv"
)Arguments
- infunc_drugName
Character. Drug name.
- infunc_cancerType
Character. Cancer type for training. Default = "PanCan".
- exclude_cancer
Character. Cancer type to exclude. Default = "PanCan".
- infunc_features
Character vector. Feature gene names (ranked).
- single_best
Character. Name of the single best feature. Default = infunc_features[1].
- k_features
Integer. Number of top features to use. Default = 100.
- infunc_alpha
Numeric. Alpha for glmnet (not used directly, tuning via alpha_gradient). Default = 1.
- model_type
Character. Model type: "glmnet" or "rf". Default = "glmnet".
- num_folds
Integer. Number of CV folds. Default = 3.
- num_tree
Integer. Number of trees for random forest. Default = 500.
- seed
Integer. Random seed for training. Default = 1.
- alpha_gradient
Numeric. Step size for alpha grid in glmnet tuning. Default = 0.05.
- lambda_gradient
Integer. Number of lambda values in glmnet tuning grid. Default = 20.
- lambda_range
Numeric vector of length 2. Min and max lambda for tuning grid. Default = c(0.0001, 1).
- cv_method
Character. Cross-validation method for caret::trainControl. Default = "cv".