# Path Configuration from tools.preprocess import * # Processing context trait = "Bladder_Cancer" cohort = "GSE185264" # Input paths in_trait_dir = "../DATA/GEO/Bladder_Cancer" in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE185264" # Output paths out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE185264.csv" out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE185264.csv" out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE185264.csv" json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # Yes - RNA quantification data from NanoString nCounter platform is_gene_available = True # 2. Variable Availability and Row Numbers trait_row = 12 # recurrence data available age_row = None # age not available gender_row = 7 # gender data available # 2.2 Conversion Functions def convert_trait(value: str) -> Optional[int]: """Convert recurrence status to binary (0: no recurrence, 1: recurrence)""" if pd.isna(value) or value == "NA": return None value = value.split(": ")[-1] if value == "No.Recurrence": return 0 elif value == "Recurrence": return 1 return None def convert_age(value: str) -> Optional[float]: """Convert age to float - not used since age data unavailable""" return None def convert_gender(value: str) -> Optional[int]: """Convert gender to binary (0: female, 1: male)""" if pd.isna(value): return None value = value.split(": ")[-1] if value == "F": return 0 elif value == "M": return 1 return None # 3. Save Metadata is_trait_available = trait_row is not None _ = validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=is_trait_available ) # 4. Extract Clinical Features selected_clinical_df = geo_select_clinical_features( clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait, age_row=age_row, convert_age=convert_age, gender_row=gender_row, convert_gender=convert_gender ) # Preview the selected features print(preview_df(selected_clinical_df)) # Save clinical data selected_clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file gene_data = get_genetic_data(matrix_file) # Print first 20 row IDs and shape of data to help debug print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of data:") print(gene_data.head()) print("\nFirst 20 gene/probe identifiers:") print(gene_data.index[:20]) # Inspect a snippet of raw file to verify identifier format import gzip with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: lines = [] for i, line in enumerate(f): if "!series_matrix_table_begin" in line: # Get the next 5 lines after the marker for _ in range(5): lines.append(next(f).strip()) break print("\nFirst few lines after matrix marker in raw file:") for line in lines: print(line) requires_gene_mapping = False # 1. Normalize gene symbols and save normalized gene data gene_data.index = gene_data.index.str.replace('-mRNA', '') gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features and remove them if needed is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Validate and save cohort info is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=True, is_biased=is_biased, df=linked_data, note="NanoString nCounter RNA profiling data for bladder cancer recurrence study" ) # 6. 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)