# Path Configuration from tools.preprocess import * # Processing context trait = "Epilepsy" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/z3/preprocess/Epilepsy/TCGA.csv" out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/TCGA.csv" out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/TCGA.csv" json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json" # Step 1: Initial Data Loading import os # Step 1: Select the most relevant TCGA cohort directory for the trait "Epilepsy" synonyms = ["epilepsy", "seizure", "seizures", "ictal", "epileptic"] all_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))] matched_dirs = [d for d in all_dirs if any(s in d.lower() for s in synonyms)] selected_cohort_dirname = None if matched_dirs: # Prefer exact 'epilepsy' match if present; otherwise take the first matched prioritized = sorted(matched_dirs, key=lambda d: (0 if "epilepsy" in d.lower() else 1, d.lower())) selected_cohort_dirname = prioritized[0] if selected_cohort_dirname is None: print("No suitable TCGA cohort found for the trait 'Epilepsy'. Skipping this trait.") # Record unusable dataset status _ = validate_and_save_cohort_info( is_final=False, cohort="TCGA", info_path=json_path, is_gene_available=False, is_trait_available=False ) clinical_df = None gene_df = None else: # Step 2: Identify clinical and genetic data file paths cohort_dir = os.path.join(tcga_root_dir, selected_cohort_dirname) clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) # Step 3: Load both files as DataFrames clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer') gene_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer') # Step 4: Print column names of the clinical data print(list(clinical_df.columns))