# Path Configuration from tools.preprocess import * # Processing context trait = "Uterine_Carcinosarcoma" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/TCGA.csv" out_gene_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/gene_data/TCGA.csv" out_clinical_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/clinical_data/TCGA.csv" json_path = "./output/preprocess/3/Uterine_Carcinosarcoma/cohort_info.json" # Review all cohort directories cohorts = os.listdir(tcga_root_dir) cohorts = [c for c in cohorts if not c.startswith('.') and not c.endswith('.ipynb')] # Choose uterine carcinosarcoma cohort since it directly matches our target trait cohort_dir = "TCGA_Uterine_Carcinosarcoma_(UCS)" cohort_path = os.path.join(tcga_root_dir, cohort_dir) # Get clinical and genetic data file paths clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_path) # Load the data files 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 demographic columns candidate_age_cols = ['age_at_initial_pathologic_diagnosis'] candidate_gender_cols = ['gender'] # Get clinical data path print("\nAge columns preview:") print({'age_at_initial_pathologic_diagnosis': ['54', '69', '73', '67', '86']}) print("\nGender columns preview:") print({'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}) # Analyze the age column candidates age_col = 'age_at_initial_pathologic_diagnosis' # Contains valid numeric age values # Analyze the gender column candidates gender_col = 'gender' # Contains standard gender labels # Print chosen columns print(f"Chosen age column: {age_col}") print(f"Chosen gender column: {gender_col}") # Select appropriate demographic columns age_col = 'age_at_initial_pathologic_diagnosis' # This is more directly usable than days_to_birth gender_col = 'gender' # 1. Extract and standardize clinical features selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col) # 2. Normalize gene symbols in genetic data normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_genetic_df.to_csv(out_gene_data_file) # 3. Link clinical and genetic data linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True) # 4. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 5. Check for bias in trait and demographic features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 6. Validate and save cohort info note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}" 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=linked_data, note=note ) # 7. 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}") print("Shape of final linked data:", linked_data.shape) else: print("Dataset was found to be unusable and was not saved")