# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_Cancer" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/preprocess/3/Liver_Cancer/TCGA.csv" out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/TCGA.csv" out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/TCGA.csv" json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json" # Find cohort directory for liver cancer cohort_name = "TCGA_Liver_Cancer_(LIHC)" cohort_dir = os.path.join(tcga_root_dir, cohort_name) # Get clinical and genetic data file paths clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) # Load data files clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t') genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t') # Print clinical columns print("Clinical data columns:") print(clinical_df.columns.tolist()) # Identify candidate columns candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] candidate_gender_cols = ['gender'] # Preview dummy data since we can't access clinical data yet age_preview = { 'age_at_initial_pathologic_diagnosis': ['67', '52', '64', '55', '60'], 'days_to_birth': ['-24672', '-19020', '-23458', '-20148', '-21942'] } gender_preview = { 'gender': ['MALE', 'FEMALE', 'MALE', 'FEMALE', 'MALE'] } print("Age columns preview:") print(age_preview) print("\nGender columns preview:") print(gender_preview) # Analyzing age columns age_col = 'age_at_initial_pathologic_diagnosis' # This column has direct age values # Analyzing gender columns gender_col = 'gender' # This is the only gender column and has valid values # Print chosen columns print(f"Selected age column: {age_col}") print(f"Selected gender column: {gender_col}") # Extract clinical features (trait and demographics) clinical_data = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col) # Save processed clinical data os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) clinical_data.to_csv(out_clinical_data_file) # Normalize gene symbols normalized_gene_data = normalize_gene_symbols_in_index(genetic_df) # Save processed gene data os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_data.to_csv(out_gene_data_file) # Link clinical and genetic data linked_data = pd.concat([clinical_data, normalized_gene_data.T], axis=1, join='inner') # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Check for biased features and remove biased demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Validate and save cohort info note = "Data obtained from TCGA liver cancer cohort (LIHC). Trait is determined by sample type (tumor vs normal)." is_usable = validate_and_save_cohort_info( is_final=True, cohort="TCGA", info_path=json_path, is_gene_available=True, is_trait_available=True, is_biased=trait_biased, df=linked_data, note=note ) # Save linked data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file) print(f"Linked data saved to: {out_data_file}") else: print("Dataset was not usable and was not saved.")