IGTD

This repository demonstrates how to use the IMPROVE library v0.1.0-2024-09-27 for building a drug response prediction (DRP) model using IGTD, 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.

Conda yml file environment.yml

ML framework:

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

IMPROVE dependencies:

• IMPROVE 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

Model scripts and parameter file

• IGTD_preprocess_improve.py - takes benchmark data files and transforms into files for training and inference
• IGTD_train_improve.py - trains the IGTD model
• IGTD_infer_improve.py - runs inference with the trained IGTD model
• model_params_def.py - definitions of parameters that are specific to the model
• IGTD_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/IGTD
cd IGTD
git checkout v0.1.0-2024-09-27

2. Set computational environment

Create conda env using yml

conda env create -f environment.yml -n IGTD
conda activate IGTD

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 IGTD model repo
3. Set up PYTHONPATH (adds IMPROVE repo).

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

python IGTD_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.pkl, val_data.pkl, test_data.pkl
• 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

ml_data
└── GDSCv1-GDSCv1
    └── split_4
        ├── Image_Data
        ├── CancID_Mapping.txt
        ├── Unique_CancID_Data.txt
        ├── Unique_DrugID_Data.txt  
        ├── DrugID_Mapping.txt 
        ├── test_y_data.csv  
        ├── train_y_data.csv
        ├── val_y_data.csv        
        ├── test_data.pkl  
        ├── train_data.pkl     
        └── val_data.pkl

5. Train IGTD model

python IGTD_train_improve.py --input_dir exp_result --output_dir exp_result

Trains IGTD using the model input data: train_data.pkl (training), val_data.pkl (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

out_models
└── GDSCv1
    └── split_4
        ├── model.h5
        ├── log.csv
        ├── Prediction_Result_Train_.txt  
        ├── Prediction_Result_Val_.txt
        ├── val_scores.json
        └── val_y_data_predicted.csv

6. Run inference on test data with the trained model

python IGTD_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

out_infer
└── GDSCv1-GDSCv1
    └── split_4
        ├── Prediction_Result_Test_.txt
        ├── test_scores.json
        └── test_y_data_predicted.csv