# Path Configuration from tools.preprocess import * # Processing context trait = "Sickle_Cell_Anemia" # Input paths tcga_root_dir = "../DATA/TCGA" # Output paths out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/TCGA.csv" out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/TCGA.csv" out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/TCGA.csv" json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" # Select TCGA_Acute_Myeloid_Leukemia_(LAML) since it is the most relevant blood disorder dataset cohort_dir = os.path.join(tcga_root_dir, "TCGA_Acute_Myeloid_Leukemia_(LAML)") # Get file paths for clinical and genetic data clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) # 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 columns for examination print("Clinical data columns:") print(clinical_df.columns.tolist()) # Define candidate columns candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] candidate_gender_cols = ['gender'] # Get file paths using proper directory structure cohort_dir = tcga_root_dir clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir) # Read and preview clinical data clinical_data = pd.read_csv(clinical_file_path, index_col=0) # Extract and preview age columns age_preview = {} for col in candidate_age_cols: age_preview[col] = clinical_data[col].head(5).tolist() print("Age columns preview:") print(age_preview) # Extract and preview gender columns gender_preview = {} for col in candidate_gender_cols: gender_preview[col] = clinical_data[col].head(5).tolist() print("\nGender columns preview:") print(gender_preview) # Select TCGA_Acute_Myeloid_Leukemia_(LAML) since it is the most relevant blood disorder dataset cohort_dir = os.path.join(tcga_root_dir, "TCGA_Acute_Myeloid_Leukemia_(LAML)") # Get file paths for clinical and genetic data clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) # 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 columns for examination print("Clinical data columns:") print(clinical_df.columns.tolist()) # Inspect previously given clinical data columns age_col = 'age_at_initial_pathologic_diagnosis' # Clear age column exists gender_col = 'gender' # Clear gender column exists # Print the chosen column names print(f"Selected age column: {age_col}") print(f"Selected 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")