GraphDRP

This repository demonstrates how to use the IMPROVE library v0.1.0 for building a drug response prediction (DRP) model using GraphDRP, and provides examples with the benchmark cross-study analysis (CSA) dataset.

This version, tagged as v0.1.0, introduces a new API which is designed to encourage broader adoption of IMPROVE and its curated models by the research community.

Dependencies

Installation instructions are detialed below in Step-by-step instructions.

Conda yml file conda_wo_candle.yml

ML framework:

• Torch - deep learning framework for building the prediction model
• Pytorch_geometric - graph neural networks (GNN)

IMPROVE dependencies:

• IMPROVE tag v0.1.0

Dataset

Benchmark data for cross-study analysis (CSA) can be downloaded from this site.

The data tree is shown below:

csa_data/raw_data/
├── splits
│   ├── CCLE_all.txt
│   ├── CCLE_split_0_test.txt
│   ├── CCLE_split_0_train.txt
│   ├── CCLE_split_0_val.txt
│   ├── CCLE_split_1_test.txt
│   ├── CCLE_split_1_train.txt
│   ├── CCLE_split_1_val.txt
│   ├── ...
│   ├── GDSCv2_split_9_test.txt
│   ├── GDSCv2_split_9_train.txt
│   └── GDSCv2_split_9_val.txt
├── x_data
│   ├── cancer_copy_number.tsv
│   ├── cancer_discretized_copy_number.tsv
│   ├── cancer_DNA_methylation.tsv
│   ├── cancer_gene_expression.tsv
│   ├── cancer_miRNA_expression.tsv
│   ├── cancer_mutation_count.tsv
│   ├── cancer_mutation_long_format.tsv
│   ├── cancer_mutation.parquet
│   ├── cancer_RPPA.tsv
│   ├── drug_ecfp4_nbits512.tsv
│   ├── drug_info.tsv
│   ├── drug_mordred_descriptor.tsv
│   └── drug_SMILES.tsv
└── y_data
    └── response.tsv

Note that ./_original_data contains data files that were used to train and evaluate the GraphDRP for the original paper.

Model scripts and parameter file

• graphdrp_preprocess_improve.py - takes benchmark data files and transforms into files for trianing and inference
• graphdrp_train_improve.py - trains the GraphDRP model
• graphdrp_infer_improve.py - runs inference with the trained GraphDRP model
• model_params_def.py - definitions of parameters that are specific to the model
• graphdrp_params.txt - default parameter file (parameter values specified in this file override the defaults)

Step-by-step instructions

1. Clone the model repository and checkout the branch (or tag)

git clone [email protected]:JDACS4C-IMPROVE/GraphDRP.git
cd GraphDRP
git checkout v0.1.0

2. Set computational environment

Option 1: create conda env using yml

conda env create -f graphdrp_environment.yml

Option 2: use conda_env_py37.sh

3. Run setup_improve.sh.

source setup_improve.sh

This will:

1. Download cross-study analysis (CSA) benchmark data into ./csa_data/.
2. Clone IMPROVE repo (and checkout v0.1.0) outside the GraphDRP model repo
3. Set up PYTHONPATH (adds IMPROVE repo).

4. Preprocess CSA benchmark data (raw data) to construct model input data (ML data)

python graphdrp_preprocess_improve.py --input_dir ./csa_data/raw_data --output_dir exp_result

Preprocesses the CSA data and creates train, validation (val), and test datasets.

Generates:

• three model input data files: train_data.pt, val_data.pt, test_data.pt
• three tabular data files, each containing the drug response values (i.e. AUC) and corresponding metadata: train_y_data.csv, val_y_data.csv, test_y_data.csv

exp_result
├── param_log_file.txt
├── processed
│   ├── test_data.pt
│   ├── train_data.pt
│   └── val_data.pt
├── test_y_data.csv
├── train_y_data.csv
├── val_y_data.csv
└── x_data_gene_expression_scaler.gz

5. Train GraphDRP model

python graphdrp_train_improve.py --input_dir exp_result --output_dir exp_result

Trains GraphDRP using the model input data: train_data.pt (training), val_data.pt (for early stopping).

Generates:

• trained model: model.pt
• predictions on val data (tabular data): val_y_data_predicted.csv
• prediction performance scores on val data: val_scores.json

exp_result
├── history.csv
├── model.pt
├── param_log_file.txt
├── processed
│   ├── test_data.pt
│   ├── train_data.pt
│   └── val_data.pt
├── test_y_data.csv
├── train_y_data.csv
├── val_scores.json
├── val_y_data.csv
├── val_y_data_predicted.csv
└── x_data_gene_expression_scaler.gz

6. Run inference on test data with the trained model

python graphdrp_infer_improve.py --input_data_dir exp_result --input_model_dir exp_result --output_dir exp_result --calc_infer_score true

Evaluates the performance on a test dataset with the trained model.

Generates:

• predictions on test data (tabular data): test_y_data_predicted.csv
• prediction performance scores on test data: test_scores.json

exp_result
├── history.csv
├── model.pt
├── param_log_file.txt
├── processed
│   ├── test_data.pt
│   ├── train_data.pt
│   └── val_data.pt
├── test_scores.json
├── test_y_data.csv
├── test_y_data_predicted.csv
├── train_y_data.csv
├── val_scores.json
├── val_y_data.csv
├── val_y_data_predicted.csv
└── x_data_gene_expression_scaler.gz