# Path Configuration from tools.preprocess import * # Processing context trait = "Epilepsy" cohort = "GSE63808" # Input paths in_trait_dir = "../DATA/GEO/Epilepsy" in_cohort_dir = "../DATA/GEO/Epilepsy/GSE63808" # Output paths out_data_file = "./output/preprocess/3/Epilepsy/GSE63808.csv" out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE63808.csv" out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE63808.csv" json_path = "./output/preprocess/3/Epilepsy/cohort_info.json" # Get paths to the SOFT and matrix files soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values for each feature (row) in clinical data unique_values_dict = get_unique_values_by_row(clinical_data) # Print background info print("=== Dataset Background Information ===") print(background_info) print("\n=== Sample Characteristics ===") print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability # From the title and summary, this dataset studies hippocampal tissue gene expression in epilepsy patients is_gene_available = True # 2.1 Variable Availability # Trait (epilepsy): Available in row 1 as binary phenotype trait_row = 1 # Age: Not available in characteristics data age_row = None # Gender: Not available in characteristics data gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x: str) -> int: """Convert epilepsy status to binary""" if not isinstance(x, str): return None # Extract value after colon value = x.split(": ")[-1].lower().strip() # Only epilepsy cases are included based on summary return 1 if value == "epilepsy" else None def convert_age(x: str) -> float: """Convert age to float - not used since age not available""" return None def convert_gender(x: str) -> int: """Convert gender to binary - not used since gender not available""" return None # 3. Save Initial 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 if trait_row is not None: clinical_features = 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 extracted features print("Preview of clinical features:") print(preview_df(clinical_features)) # Save to CSV clinical_features.to_csv(out_clinical_data_file)