PathDSP

This repository demonstrates how to use the IMPROVE library v0.1.0-2024-09-27 for building a drug response prediction (DRP) model using PathDSP, 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 detailed below in Step-by-step instructions.

Conda yml file PathDSP_env_conda

ML framework:

• Torch -- 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

• PathDSP_preprocess_improve.py - takes benchmark data files and transforms into files for training and inference
• PathDSP_train_improve.py - trains the PathDSP model
• PathDSP_infer_improve.py - runs inference with the trained PathDSP model
• model_params_def.py - definitions of parameters that are specific to the model
• PathDSP_params.txt - default parameter file

Step-by-step instructions

1. Clone the model repository

git clone https://github.com/JDACS4C-IMPROVE/PathDSP
cd PathDSP
git checkout v0.1.0-2024-09-27

2. Set computational environment

Create conda env using yml

conda env create -f PathDSP_env_conda.yml -n PathDSP_env
conda activate PathDSP_env

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 tag v0.1.0-2024-09-27) outside the PathDSP model repo
3. Set up env variables: IMPROVE_DATA_DIR (to ./csa_data/) and PYTHONPATH (adds IMPROVE repo).
4. Download the model-specific supplemental data (aka author data) and set up the env variable AUTHOR_DATA_DIR.

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

python PathDSP_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.txt, val_data.txt, test_data.txt

exp_result
├── tmpdir_ssgsea
├── EXP.txt
├── cnv_data.txt
├── CNVnet.txt
├── DGnet.txt
├── MUTnet.txt
├── drug_mbit_df.txt
├── drug_target.txt
├── mutation_data.txt 
├── test_data.txt
├── train_data.txt
├── val_data.txt
└── x_data_gene_expression_scaler.gz

5. Train PathDSP model

python PathDSP_train_improve.py --input_dir exp_result --output_dir exp_result

Trains PathDSP using the model input data: train_data.txt (training), val_data.txt (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
├── model.pt
├── checkpoint.pt
├── Val_Loss_orig.txt
├── val_scores.json
└── val_y_data_predicted.csv

6. Run inference on test data with the trained model

python PathDSP_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
├── test_scores.json
└── test_y_data_predicted.csv