# Path Configuration from tools.preprocess import * # Processing context trait = "COVID-19" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/preprocess/3/COVID-19/TCGA.csv" out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/TCGA.csv" out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/TCGA.csv" json_path = "./output/preprocess/3/COVID-19/cohort_info.json" # Find the lung-related subdirectories as most relevant to COVID-19 lung_dirs = [d for d in os.listdir(tcga_root_dir) if 'LUNG' in d] if not lung_dirs: is_usable = validate_and_save_cohort_info(is_final=False, cohort="TCGA", info_path=json_path, is_gene_available=False, is_trait_available=False) raise ValueError("No suitable TCGA cohort found for COVID-19") # Select the most specific lung cancer cohort cohort_dir = os.path.join(tcga_root_dir, "TCGA_Lung_Cancer_(LUNG)") # Get relevant file paths clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) # Load clinical data clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t') # Load genetic data genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t') # Print clinical data columns print("Clinical data columns:") print(clinical_df.columns.tolist()) # Step 1: Define candidate columns candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] candidate_gender_cols = ['gender'] # Step 2: Navigate directory structure and get data clinical_file_path = None for subdir in os.listdir(tcga_root_dir): subdir_path = os.path.join(tcga_root_dir, subdir) if os.path.isdir(subdir_path): try: clinical_file_path, _ = tcga_get_relevant_filepaths(subdir_path) if clinical_file_path: break except: continue if clinical_file_path: clinical_data = pd.read_csv(clinical_file_path, index_col=0, delimiter='\t') # Preview age columns age_preview = {} for col in candidate_age_cols: if col in clinical_data.columns: age_preview[col] = clinical_data[col].head(5).tolist() print("Age columns preview:") print(age_preview) # Preview gender columns gender_preview = {} for col in candidate_gender_cols: if col in clinical_data.columns: gender_preview[col] = clinical_data[col].head(5).tolist() print("\nGender columns preview:") print(gender_preview) else: print("No clinical data file found") # Select appropriate age and gender columns age_col = 'age_at_initial_pathologic_diagnosis' # Contains direct age values gender_col = 'gender' # Contains clear gender values # Print chosen columns print(f"Selected age column: {age_col}") print(f"Selected gender column: {gender_col}") # Early validation that this dataset is not suitable for COVID-19 is_usable = validate_and_save_cohort_info( is_final=False, cohort="TCGA", info_path=json_path, is_gene_available=True, is_trait_available=False, # TCGA data lacks COVID-19 trait information note="TCGA cancer data cannot be repurposed for COVID-19 analysis" ) # Exit early since this dataset is not suitable raise ValueError("TCGA data is not suitable for COVID-19 analysis. This trait will be skipped.")