This repository contains the code to train the drug-response models used in "A machine learning framework for mining mechanistic insights and predicting response to therapy in cancer" by Vougas, Sakellaropoulos et al.
The main script tune_drug.R is run independently for each drug. The man page
of the script is:
Run training pipeline
positional arguments:
drug Drug to learn
matrix Matrix to train on (RDS file)
hyperparams json file with hyper parameters
prefix prefix to write files
flags:
-h, --help show this help message and exit
optional arguments:
-x, --opts OPTS RDS file containing argument values
-m, --max_models MAX_MODELS Max number of models to compute [default: 15]
-v, --varcut VARCUT Percent of genes to keep based on variance. [default: 100]
-l, --learner LEARNER Algorithm to use for learning (DL, RF, GLM) [default: DL]
-t, --test TEST Path to clinical data
-k, --kfold KFOLD Number of CV-splits [default: 5]
--max_mem MAX_MEM H2O parameter [default: 64G]
-c, --cores CORES Method to determine cores with future::availableCores [default: Slurm]
drug must be one of the columns of matrix the rest are treated as genes.
The hyperparameters used in the paper are specifed in hyperparams/.
All of them are specified as lists in order to be able to expand the grid.
The script batch_correct.R is used to correct for batch effects between the training and
test dataset if required.