# Path Configuration from tools.preprocess import * # Processing context trait = "Hemochromatosis" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/preprocess/3/Hemochromatosis/TCGA.csv" out_gene_data_file = "./output/preprocess/3/Hemochromatosis/gene_data/TCGA.csv" out_clinical_data_file = "./output/preprocess/3/Hemochromatosis/clinical_data/TCGA.csv" json_path = "./output/preprocess/3/Hemochromatosis/cohort_info.json" # Get subdirectories from TCGA root directory tcga_subdirs = os.listdir(tcga_root_dir) tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')] # Review cohort directories for liver data since Hemochromatosis affects liver selected_cohort = 'TCGA_Liver_Cancer_(LIHC)' if selected_cohort not in tcga_subdirs: # No suitable cohort found - record this and end processing validate_and_save_cohort_info( is_final=False, cohort="TCGA", info_path=json_path, is_gene_available=True, is_trait_available=False ) else: # Get file paths for clinical and genetic data cohort_dir = os.path.join(tcga_root_dir, selected_cohort) clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) # Load the data clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t') genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t') # Print clinical data columns print("Clinical data columns:") print(clinical_df.columns.tolist()) # Define candidate columns for age and gender candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] candidate_gender_cols = ['gender'] # Preview the data by extracting candidate columns from clinical data preview_dict = {} if len(candidate_age_cols) > 0: age_data = clinical_df[candidate_age_cols].head() preview_dict.update({col: age_data[col].tolist() for col in candidate_age_cols}) if len(candidate_gender_cols) > 0: gender_data = clinical_df[candidate_gender_cols].head() preview_dict.update({col: gender_data[col].tolist() for col in candidate_gender_cols}) # Display preview print(preview_dict) # Inspect available columns for age and gender information age_col = 'age_at_initial_pathologic_diagnosis' # This column has clear age values gender_col = 'gender' # This column has clear gender labels # Print chosen columns print(f"Chosen age column: {age_col}") print(f"Chosen gender column: {gender_col}") # Extract and standardize clinical features selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col) selected_clinical_df.to_csv(out_clinical_data_file) # Normalize gene symbols and save normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df) normalized_genetic_df.to_csv(out_gene_data_file) # Link clinical and genetic data linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1) # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Judge whether features are biased and remove biased demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Final validation and save cohort info note = "Used liver cancer (LIHC) data as a proxy for hemochromatosis since both affect liver function" 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: linked_data.to_csv(out_data_file)