DeepCDR

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

This version, tagged as v0.1.0-2024-09-27, introduces a new API which is designed to encourage broader adoption of IMPROVE and its curated models by the research community.

Dependencies

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

ML framework:

• Tensorflow -- deep learning framework for building the prediction model

IMPROVE dependencies:

• IMPROVE tag v0.1.0-2024-09-27

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_work folder contains data files and scripts used to train and evaluate the DeepCDR for the original paper.

Model scripts and parameter file

• deepcdr_preprocess_improve.py - takes benchmark data files and transforms into files for trianing and inference
• deepcdr_train_improve.py - trains a deepcdr DRP model
• deepcdr_infer_improve.py - runs inference with the trained deepcdr model
• model_params_def.py - definitions of parameters that are specific to the model
• deepcdr_params.txt - default parameter file (parameter values specified in this file override the defaults)

Step-by-step instructions

1. Clone the model repository

git clone https://github.com/JDACS4C-IMPROVE/DeepCDR.git
cd DeepCDR
git checkout develop

2. Set computational environment

Option 1: Create the conda env using the yml file.

conda env create -f parsl_env.yml

Option 2: Use the following commands to create the environment.

conda create --name DeepCDR_IMPROVE_env python=3.10
conda activate DeepCDR_IMPROVE_env
conda install tensorflow-gpu=2.10.0
pip install rdkit==2023.9.6
pip install deepchem==2.8.0
pip install PyYAML

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 (checkout develop) outside the LGBM model repo.
3. Set up env variables: IMPROVE_DATA_DIR (to ./csa_data/) and PYTHONPATH (adds IMPROVE repo).

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

python deepcdr_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:

• five model input data files: cancer_dna_methy_model, cancer_gen_expr_model, cancer_gen_mut_model, drug_features.pickle, norm_adj_mat.pickle
• 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
 ├── cancer_dna_methy_model
 ├── cancer_gen_expr_model
 ├── cancer_gen_mut_model
 ├── test_y_data.csv
 ├── train_y_data.csv
 ├── val_y_data.csv
 ├── drug_features.pickle
 └── norm_adj_mat.pickle

5. Train DeepCDR model

python deepcdr_train_improve.py --input_dir exp_result --output_dir exp_result

Trains DeepCDR using the model input data generated in the previous step.

Generates:

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

exp_result
 ├── param_log_file.txt
 ├── cancer_dna_methy_model
 ├── cancer_gen_expr_model
 ├── cancer_gen_mut_model
 ├── test_y_data.csv
 ├── train_y_data.csv
 ├── val_y_data.csv
 ├── drug_features.pickle
 ├── norm_adj_mat.pickle
 ├── DeepCDR_model
 ├── val_scores.json
 └── val_y_data_predicted.csv

6. Run inference on test data with the trained model

python deepcdr_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
 ├── param_log_file.txt
 ├── cancer_dna_methy_model
 ├── cancer_gen_expr_model
 ├── cancer_gen_mut_model
 ├── test_y_data.csv
 ├── train_y_data.csv
 ├── val_y_data.csv
 ├── drug_features.pickle
 ├── norm_adj_mat.pickle
 ├── DeepCDR_model
 ├── val_scores.json
 ├── val_y_data_predicted.csv
 ├── test_scores.json
 └── test_y_data_predicted.csv