# Path Configuration from tools.preprocess import * # Processing context trait = "Longevity" cohort = "GSE16717" # Input paths in_trait_dir = "../DATA/GEO/Longevity" in_cohort_dir = "../DATA/GEO/Longevity/GSE16717" # Output paths out_data_file = "./output/preprocess/3/Longevity/GSE16717.csv" out_gene_data_file = "./output/preprocess/3/Longevity/gene_data/GSE16717.csv" out_clinical_data_file = "./output/preprocess/3/Longevity/clinical_data/GSE16717.csv" json_path = "./output/preprocess/3/Longevity/cohort_info.json" # Step 1: Get file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Step 2: Extract background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Step 3: Get dictionary of unique values for each clinical feature unique_values_dict = get_unique_values_by_row(clinical_data) # Step 4: Print background info and sample characteristics print("Dataset Background Information:") print("-" * 80) print(background_info) print("\nSample Characteristics:") print("-" * 80) print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability # Yes, this is a gene expression study based on the background information is_gene_available = True # 2.1 Data Availability # Key 0 contains "group" info that can determine longevity status trait_row = 0 # Key 2 contains age information age_row = 2 # Key 1 contains gender information gender_row = 1 # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> Optional[int]: """Convert long-lived status to binary.""" if not isinstance(value, str): return None value = value.split(": ")[-1].lower().strip() if "long-lived" in value: return 1 elif "control" in value: return 0 elif "offspring" in value: return 0 return None def convert_age(value: str) -> Optional[float]: """Convert age string to float.""" if not isinstance(value, str): return None try: # Extract numeric value before "years" age = float(value.split(": ")[-1].split(" ")[0]) return age except: return None def convert_gender(value: str) -> Optional[int]: """Convert gender to binary (0=female, 1=male).""" if not isinstance(value, str): return None value = value.split(": ")[-1].lower().strip() 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. Clinical Feature Extraction clinical_df = geo_select_clinical_features(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 clinical data preview = preview_df(clinical_df) # Save clinical data clinical_df.to_csv(out_clinical_data_file) # 1. Extract gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # 2. Print first 20 row IDs print("First 20 gene/probe identifiers:") print(genetic_data.index[:20]) # The identifiers appear to be Affymetrix probe IDs rather than human gene symbols # They are numerical values that need to be mapped to gene symbols requires_gene_mapping = True # 1. Extract gene annotation data from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # 2. Preview annotation data print("Column names and first few values in gene annotation data:") print(preview_df(gene_annotation)) # First check all available columns in the annotation data print("Available columns in the gene annotation data:") print(gene_annotation.columns.tolist()) # Check if there are more sections in the SOFT file print("\nChecking for additional annotation sections in SOFT file...") with gzip.open(soft_file_path, 'rt') as f: first_1000_lines = ''.join([next(f) for _ in range(1000)]) print(first_1000_lines) # Based on the SOFT file review, we need to modify gene annotation extraction # Let's extract annotation with a different set of prefixes to get more comprehensive data gene_annotation = get_gene_annotation(soft_file_path, prefixes=['#', '!']) # Check columns in new annotation data print("\nColumns in expanded annotation data:") print(gene_annotation.columns.tolist()) print("\nSample records:") print(gene_annotation.head().to_dict('records')) # For now, we'll save the intermediate probe-level data # This indicates the dataset needs additional processing to map to human gene symbols genetic_data.to_csv(out_gene_data_file) print("\nINFO: The gene identifiers in this dataset require additional processing steps to map to human gene symbols.") print("The probe-level data has been saved for further processing.") # 1. Normalize gene symbols and save gene data normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features and remove biased demographic ones is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and save metadata 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="Longevity status based on group classification (long-lived sibs vs controls)" ) # 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)