# Path Configuration from tools.preprocess import * # Processing context trait = "Melanoma" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/preprocess/3/Melanoma/TCGA.csv" out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/TCGA.csv" out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/TCGA.csv" json_path = "./output/preprocess/3/Melanoma/cohort_info.json" # Find melanoma data directory cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)") # Get paths to clinical and genetic data files clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir) # Load the data files clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t') # Print clinical columns print("Clinical data columns:") print(clinical_df.columns.tolist()) # Mark data as available is_gene_available = True is_trait_available = True validate_and_save_cohort_info( is_final=False, cohort="TCGA", info_path=json_path, is_gene_available=is_gene_available, is_trait_available=is_trait_available ) # Identify candidate columns candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] candidate_gender_cols = ['gender'] # Get correct file paths using library function clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "SKCM")) clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0) # Preview using library function age_preview = preview_df(clinical_df[candidate_age_cols]) print("Age columns preview:") print(age_preview) gender_preview = preview_df(clinical_df[candidate_gender_cols]) print("\nGender columns preview:") print(gender_preview) # Set TCGA cohort name trait = "SKCM" # TCGA code for Skin Cutaneous Melanoma # Get file paths cohort_dir = os.path.join(tcga_root_dir, trait) clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir) # Define candidate columns candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_at_initial_pathologic_diagnosis"] candidate_gender_cols = ["gender"] # Extract and preview age columns if available age_preview = {} if len(candidate_age_cols) > 0: clinical_data = pd.read_csv(clinical_file_path, index_col=0) for col in candidate_age_cols: if col in clinical_data.columns: age_preview[col] = clinical_data[col].head().tolist() print("Age Column Preview:", age_preview) # Extract and preview gender columns if available gender_preview = {} if len(candidate_gender_cols) > 0: if 'clinical_data' not in locals(): clinical_data = pd.read_csv(clinical_file_path, index_col=0) for col in candidate_gender_cols: if col in clinical_data.columns: gender_preview[col] = clinical_data[col].head().tolist() print("Gender Column Preview:", gender_preview) # Previous execution output contained dictionaries of age and gender column candidates # Set chosen column names for demographic information based on that data age_col = "age_at_initial_pathologic_diagnosis" gender_col = "gender" # Print the chosen columns print(f"Selected Age Column: {age_col}") print(f"Selected Gender Column: {gender_col}") # Get paths cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)") clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir) # Load data clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t') # Extract clinical features selected_clinical_df = tcga_select_clinical_features( clinical_df=clinical_df, trait=trait, age_col=age_col, gender_col=gender_col ) # Normalize gene symbols normalized_gene_df = normalize_gene_symbols_in_index(genetic_df) # Save normalized gene data os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_df.to_csv(out_gene_data_file) # Link clinical and genetic data linked_data = pd.merge( selected_clinical_df, normalized_gene_df.T, left_index=True, right_index=True ) # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Check for bias and remove biased demographic features is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait) # Final validation and save metadata 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=is_biased, df=cleaned_data, note="This dataset contains TCGA melanoma data with normalized gene expression values" ) # Save processed data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) cleaned_data.to_csv(out_data_file)