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main_case_study.py
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| import argparse | |||
| import numpy as np | |||
| import pandas as pd | |||
| import torch | |||
| import scipy.sparse as sp | |||
| from sklearn.model_selection import KFold | |||
| from sklearn.metrics import roc_auc_score, average_precision_score | |||
| from model import DeepTraCDR, Optimizer | |||
| from utils import evaluate_auc, common_data_index | |||
| from data_sampler import TargetSampler | |||
| from data_loader import load_data | |||
| # Clear CUDA cache to optimize memory usage | |||
| torch.cuda.empty_cache() | |||
| def main(): | |||
| # Parse command-line arguments for model configuration | |||
| parser = argparse.ArgumentParser(description="DeepTraCDR Case Study for Drug Response Prediction") | |||
| parser.add_argument('-device', type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", | |||
| help="Device to run the model (cuda:0 or cpu)") | |||
| parser.add_argument('-data', type=str, default='gdsc', | |||
| help="Dataset to use (e.g., gdsc or ccle)") | |||
| parser.add_argument('--wd', type=float, default=1e-4, help="Weight decay for optimizer") | |||
| parser.add_argument('--layer_size', nargs='+', type=int, default=[512], | |||
| help="List of layer sizes for the GCN model") | |||
| parser.add_argument('--gamma', type=float, default=15, help="Gamma parameter for loss function") | |||
| parser.add_argument('--epochs', type=int, default=1000, help="Number of training epochs") | |||
| parser.add_argument('--test_freq', type=int, default=50, help="Frequency of evaluation during training") | |||
| parser.add_argument('--patience', type=int, default=100, help="Patience for early stopping") | |||
| parser.add_argument('--lr', type=float, default=0.0005, help="Learning rate for optimizer") | |||
| parser.add_argument('--k_fold', type=int, default=5, help="Number of folds for cross-validation") | |||
| args = parser.parse_args() | |||
| # Load dataset-specific drug response data | |||
| if args.data == "gdsc": | |||
| # Define target drug CIDs (e.g., Dasatinib=5330286, GSK690693=11338033) | |||
| target_drug_cids = np.array([5330286, 11338033, 24825971]) | |||
| # Load cell-drug binary response matrix | |||
| cell_drug = pd.read_csv( | |||
| "/media/external_16TB_1/ali_kianfar/Data/GDSC/cell_drug_binary.csv", | |||
| index_col=0, header=0 | |||
| ) | |||
| cell_drug.columns = cell_drug.columns.astype(np.int32) | |||
| drug_cids = cell_drug.columns.values | |||
| # Extract target drug responses and compute positive sample count | |||
| cell_target_drug = np.array(cell_drug.loc[:, target_drug_cids], dtype=np.float32) | |||
| target_pos_num = sp.coo_matrix(cell_target_drug).data.shape[0] | |||
| target_indexes = common_data_index(drug_cids, target_drug_cids) | |||
| elif args.data == "ccle": | |||
| # Define target drug CIDs for CCLE dataset | |||
| target_drug_cids = np.array([5330286]) | |||
| # Load cell-drug binary response matrix | |||
| cell_drug = pd.read_csv( | |||
| "/media/external_16TB_1/ali_kianfar/Data/CCLE/cell_drug_binary.csv", | |||
| index_col=0, header=0 | |||
| ) | |||
| cell_drug.columns = cell_drug.columns.astype(np.int32) | |||
| drug_cids = cell_drug.columns.values | |||
| # Extract target drug responses and compute positive sample count | |||
| cell_target_drug = np.array(cell_drug.loc[:, target_drug_cids], dtype=np.float32) | |||
| target_pos_num = sp.coo_matrix(cell_target_drug).data.shape[0] | |||
| target_indexes = common_data_index(drug_cids, target_drug_cids) | |||
| # Load additional data (adjacency matrix, fingerprints, expression, etc.) | |||
| full_adj, drug_fingerprints, exprs, null_mask, pos_num, args = load_data(args) | |||
| full_adj_np = full_adj.copy() # Copy for sampler usage | |||
| # Print data shapes for verification | |||
| print(f"Adjacency matrix shape: {full_adj.shape}") | |||
| print(f"Expression data shape: {exprs.shape}") | |||
| print(f"Null mask shape: {null_mask.shape}") | |||
| # Convert adjacency matrix to PyTorch tensor | |||
| if isinstance(full_adj, np.ndarray): | |||
| full_adj = torch.from_numpy(full_adj).float().to(args.device) | |||
| # Initialize k-fold cross-validation | |||
| k = args.k_fold | |||
| n_kfolds = 5 # Number of k-fold iterations | |||
| all_metrics = {'auc': [], 'auprc': []} | |||
| # Perform k-fold cross-validation | |||
| for n_kfold in range(n_kfolds): | |||
| kfold = KFold(n_splits=k, shuffle=True, random_state=n_kfold) | |||
| idx_all = np.arange(target_pos_num) | |||
| for fold, (train_idx, test_idx) in enumerate(kfold.split(idx_all)): | |||
| print(f"\n--- Fold {fold+1}/{k} (Iteration {n_kfold+1}/{n_kfolds}) ---") | |||
| # Initialize data sampler for training and testing | |||
| sampler = TargetSampler( | |||
| response_mat=full_adj_np, | |||
| null_mask=null_mask, | |||
| target_indexes=target_indexes, | |||
| pos_train_index=train_idx, | |||
| pos_test_index=test_idx | |||
| ) | |||
| # Initialize DeepTraCDR model | |||
| model = DeepTraCDR( | |||
| adj_mat=full_adj, | |||
| cell_exprs=exprs, | |||
| drug_finger=drug_fingerprints, | |||
| layer_size=args.layer_size, | |||
| gamma=args.gamma, | |||
| device=args.device | |||
| ) | |||
| # Initialize optimizer for training | |||
| opt = Optimizer( | |||
| model=model, | |||
| train_data=sampler.train_data, | |||
| test_data=sampler.test_data, | |||
| test_mask=sampler.test_mask, | |||
| train_mask=sampler.train_mask, | |||
| adj_matrix=full_adj, | |||
| evaluate_fun=evaluate_auc, | |||
| lr=args.lr, | |||
| wd=args.wd, | |||
| epochs=args.epochs, | |||
| test_freq=args.test_freq, | |||
| patience=args.patience, | |||
| device=args.device | |||
| ) | |||
| # Train the model and retrieve best metrics | |||
| true, pred, best_auc, best_auprc = opt.train() | |||
| all_metrics['auc'].append(best_auc) | |||
| all_metrics['auprc'].append(best_auprc) | |||
| print(f"Fold {fold+1}: AUC={best_auc:.4f}, AUPRC={best_auprc:.4f}") | |||
| # Compute and display average metrics across all folds | |||
| print(f"\nFinal Average Metrics (Across {n_kfolds*k} Folds):") | |||
| for metric, values in all_metrics.items(): | |||
| mean = np.mean(values) | |||
| std = np.std(values) | |||
| print(f"{metric.upper()}: {mean:.4f} ± {std:.4f}") | |||
| # Perform case study: Predict missing responses for target drugs | |||
| print("\n--- Case Study: Predicting Missing Responses for Target Drugs ---") | |||
| model.eval() | |||
| with torch.no_grad(): | |||
| final_pred, cell_emb, drug_emb = model() # Shape: [num_cells, num_drugs] | |||
| # Create a DataFrame to sort cell lines by predicted sensitivity | |||
| num_cells, num_drugs = final_pred.size() | |||
| cell_names = cell_drug.index.values # Cell line names | |||
| cid_list = cell_drug.columns.values # Drug CIDs | |||
| # Identify top 10 sensitive cell lines for each target drug | |||
| for d in range(num_drugs): | |||
| cid = cid_list[d] | |||
| if cid in [5330286, 11338033]: # Focus on Dasatinib or GSK690693 | |||
| drug_preds = final_pred[:, d].cpu().numpy() | |||
| sorted_idx = np.argsort(-drug_preds) # Sort in descending order | |||
| top_10_cells = [(cell_names[i], drug_preds[i]) for i in sorted_idx[:10]] | |||
| drug_name = "Dasatinib" if cid == 5330286 else "GSK690693" | |||
| print(f"\nTop 10 Sensitive Cell Lines for {drug_name} (CID={cid}):") | |||
| for rank, (cell, score) in enumerate(top_10_cells, start=1): | |||
| print(f"{rank}. Cell: {cell}, Score: {score:.4f}") | |||
| if __name__ == "__main__": | |||
| # Set high precision for matrix multiplication | |||
| torch.set_float32_matmul_precision('high') | |||
| main() | |||
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