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DeepTTC

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

This version, tagged as v0.1.0-alpha, is the final release before transitioning to v0.1.0-alpha, which introduces a new API. Version v0.0.3-beta and all previous releases have served as the foundation for developing essential components of the IMPROVE software stack. Subsequent releases build on this legacy with an updated API, designed to encourage broader adoption of IMPROVE and its curated models by the research community.

A more detailed tutorial can be found here.

Dependencies

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

Conda yml file environment_no_candle.yml

ML framework:

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

IMPROVE dependencies:

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 DeepTTC for the original paper.

Model scripts and parameter file

  • deepttc_preprocess_improve.py - takes benchmark data files and transforms into files for trianing and inference
  • deepttc_train_improve.py - trains the DeepTTC model
  • deepttc_infer_improve.py - runs inference with the trained DeepTTC model
  • DeepTTC.default - default parameter file

Step-by-step instructions

1. Clone the model repository

git clone git@github.com:JDACS4C-IMPROVE/DeepTTC.git
cd DeepTTC
git checkout v0.0.3-beta

2. Additional dependencies

Run python3 -m pip install -r requirements.txt

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 v0.0.3-beta) outside the DeepTTC 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 deepttc_preprocess_improve.py

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
ml_data
└── GDSCv1-CCLE
    └── split_0
        ├── 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 DeepTTC model

python deepttc_train_improve.py

Trains DeepTTC 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
out_models
└── GDSCv1
    └── split_0
        ├── best -> /lambda_stor/data/onarykov/git/DeepTTC/DeepTTC-develop/out_models/GDSCv1/split_0/epochs/002
        ├── epochs
        │   ├── 001
        │   │   ├── ckpt-info.json
        │   │   └── model.h5
        │   └── 002
        │       ├── ckpt-info.json
        │       └── model.h5
        ├── last -> /lambda_stor/data/onarykov/git/DeepTTC/DeepTTC-develop/out_models/GDSCv1/split_0/epochs/002
        ├── model.pt
        ├── out_models
        │   └── GDSCv1
        │       └── split_0
        │           └── ckpt.log
        ├── val_scores.json
        └── val_y_data_predicted.csv

6. Run inference on test data with the trained model

python deepttc_infer_improve.py

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-CCLE
    └── split_0
        ├── test_scores.json
        └── test_y_data_predicted.csv