# Path Configuration from tools.preprocess import * # Processing context trait = "Depression" cohort = "GSE128387" # Input paths in_trait_dir = "../DATA/GEO/Depression" in_cohort_dir = "../DATA/GEO/Depression/GSE128387" # Output paths out_data_file = "./output/preprocess/3/Depression/GSE128387.csv" out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE128387.csv" out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE128387.csv" json_path = "./output/preprocess/3/Depression/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. Check gene expression data availability # From background info, this is a microarray study of gene expression, not miRNA/methylation is_gene_available = True # 2.1 Identify data rows trait_row = 1 # "illness" field indicates depression status age_row = 2 # "age" field gender_row = 3 # "Sex" field # 2.2 Data conversion functions def convert_trait(value: str) -> int: """Convert depression status to binary""" if not isinstance(value, str): return None value = value.split(': ')[-1].lower() if 'major depressive disorder' in value: return 1 return None def convert_age(value: str) -> float: """Convert age to continuous value""" if not isinstance(value, str): return None try: age = float(value.split(': ')[-1]) return age except: return None def convert_gender(value: str) -> int: """Convert gender to binary (0=female, 1=male)""" if not isinstance(value, str): return None value = value.split(': ')[-1].lower() if value == 'female': return 0 elif value == 'male': return 1 return None # 3. Save metadata validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=trait_row is not None ) # 4. Extract clinical features 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 and save clinical data print("Clinical data preview:") print(preview_df(clinical_df)) clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_df = get_genetic_data(matrix_file) # Print DataFrame shape and first 20 row IDs print("DataFrame shape:", genetic_df.shape) print("\nFirst 20 row IDs:") print(genetic_df.index[:20]) print("\nPreview of first few rows and columns:") print(genetic_df.head().iloc[:, :5]) # The gene identifiers appear to be probe IDs # (numeric identifiers around 16657xxx) rather than human gene symbols requires_gene_mapping = True # Extract gene annotation data, excluding control probe lines gene_metadata = get_gene_annotation(soft_file) # Preview filtered annotation data print("Column names:") print(gene_metadata.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_metadata)) # 1. Based on observation: # - Gene expression data has identifiers like '16657436' # - In annotation data, 'ID' column has the same format identifiers # - 'gene_assignment' column contains gene symbol info in the format "//GENE_SYMBOL//" # 2. Extract ID and gene assignments, then get mapping between them mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment') # 3. Map probe IDs to gene symbols and convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_df, mapping_df) # Preview results print("Gene expression data shape:", gene_data.shape) print("\nFirst few genes and samples:") print(gene_data.head().iloc[:, :5]) # Save gene data gene_data.to_csv(out_gene_data_file) # 1. Normalize gene symbols and save 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 clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and metadata saving 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=trait_biased, df=linked_data, note="Study of depression in obese patients before and after bariatric surgery" ) # 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)