Cancer Drug Response prediction using a Recommender System (CaDRReS) is based on the matrix factorization approach to predict which drugs are sensitive for an unseen cell line. CaDRReS can also be used for studying drug response mechanisms including classes of drugs, subtypes of cell lines based on drug response profile, and drug-pathway associations.
Chayaporn Suphavilai, Denis Bertrand, Niranjan Nagarajan; Predicting Cancer Drug Response using a Recommender System, Bioinformatics, Volume 34, Issue 22, 15 November 2018, Pages 3907–3914, https://doi.org/10.1093/bioinformatics/bty452
CaDDReS is based on Python 2.7
- Pandas
- Numpy
- Scipy
- Argparse
pip install -r requirements.txtUsers have two options to run CaDRReS. The first option is to run CaDRReS_test.py for applying the pre-trained model based on GDSC dataset to predict drug response of input samples. The second option is to run CaDRReS_train_and_test.py for training and testing the model as well as optaining the pharmacogenomic space of input samples. We provide examples of both options below:
Here we provides a model trained on GDSC dataset using 10 dimensions of the pharmacogenomic space.
Input files
CaDRReS_model.pickleis a file containing existing modeldrug_response_ic50_test.csvcontains an empty matrix where rows are cell lines and columns are featurescell_line_features.csvcontains a feature matrix where rows are testing cell lines and columns are features. The features have to match with the features used for training the model.
Output file
- Drug response prediction of testing cell lines
{out_dir}/CaDRReS_pred.csv - Matrices P and Q and biases terms
{out_dir}/CaDRReS_pred.pickle
Command
python CaDRReS_test.py CaDRReS_model.pickle ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_cellline_pcor_ess_genes.csv {out_dir}An example command for predicting drug responses based on the provided model:
$ cd scripts
$ python CaDRReS_test.py ../output/10D/seed0/lr0-01/CaDRReS_model.pickle ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_cellline_pcor_ess_genes.csv ../outputInput files
drug_response_ic50_train.csvcontains a matrix of IC50s where rows are cell lines and columns are drugsdrug_response_ic50_test.csvcontains an empty matrix where rows are cell lines and columns are drugscell_line_features.csvcontains a feature matrix where rows are both testing and training cell lines and columns are featuresdrug_list.txtis a text file contains drugs of interest
Output files
- Drug response prediction of testing cell lines
{out_dir}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_pred_end.csv - Drug response prediction of training cell lines
{out_dir}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_pred_end_train.csv - A pickle file contains the model
{out_name}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_model.pickle - Cell line (P) and drug (Q) matrices
{out_name}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_P.csvand{out_name}/{f}D/seed{seed}/lr{l_rate}/CaDDReS_Q.csv
Parameters
| Description | Variable | Example |
|---|---|---|
| Number of dimensions | f | 10 |
| Output directory | out_dir | output/ |
| Maximum iterations | max_iterations | 50000 |
| Learning rate | l_rate | 0.01 |
| Random seed | seed | 0 |
Command
python CaDRReS_train_and_test.py drug_response_ic50_train.csv drug_response_ic50_test.csv cell_line_features.csv drug_list.txt {out_dir} {f} {max_iterations} {l_rate} {seed}Note that CaDRReS also saves checkpoints (parameters and predictions) of the model for every 1000 iterations. An example command to train a model for CCLE dataset:
$ cd scripts
$ python CaDRReS_train_and_test.py ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_cellline_pcor_ess_genes.csv ../misc/ccle_drugMedianGE0.txt ../output 10 100 0.01 0For calculating dose-response curves of CCLE and GDSC, please visit this page.
Please direct any questions or feedback to Chayaporn Suphavilai (suphavilaic99@gis.a-star.edu.sg) and Niranjan Nagarajan (nagarajann@gis.a-star.edu.sg).