# Path Configuration from tools.preprocess import * # Processing context trait = "Longevity" cohort = "GSE48264" # Input paths in_trait_dir = "../DATA/GEO/Longevity" in_cohort_dir = "../DATA/GEO/Longevity/GSE48264" # Output paths out_data_file = "./output/preprocess/3/Longevity/GSE48264.csv" out_gene_data_file = "./output/preprocess/3/Longevity/gene_data/GSE48264.csv" out_clinical_data_file = "./output/preprocess/3/Longevity/clinical_data/GSE48264.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 is_gene_available = True # Affymetrix gene-chips mentioned in background info # 2. Variable Availability and Row Identification trait_row = 3 # Survival status recorded in row 3 age_row = None # Age is constant at 70 years for all subjects gender_row = None # Gender data not available # Define conversion functions def convert_trait(value: str) -> Optional[int]: """Convert survival status to binary (0=alive, 1=deceased)""" if not value or ":" not in value: return None status = value.split(":")[1].strip() if status == "Death": return 1 elif status == "None": # Still alive return 0 elif status == "Hosp": # Hospitalized but not deceased return 0 return None def convert_age(value: str) -> Optional[float]: """Not used since age is constant""" return None def convert_gender(value: str) -> Optional[int]: """Not used since gender data unavailable""" 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 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 and save clinical data print(preview_df(selected_clinical_df)) selected_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]) # These numbers appear to be probe IDs, not standard human gene symbols # Human gene symbols typically follow patterns like BRCA1, TP53, IL6, etc. # This data seems to use numeric probe identifiers that will 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)) # 1. The 'ID' column in gene annotation matches probe IDs in gene expression data # The 'gene_assignment' contains gene symbol information # 2. Extract mapping between probe IDs and gene symbols def extract_first_gene_symbol(text: str) -> str: """Extract first gene symbol from gene_assignment string""" if text == '---' or pd.isna(text): return None # The format is typically: "RefSeq // GENE_SYMBOL // description" # First split by '//' and take second item which contains gene symbol parts = text.split('//') if len(parts) >= 2: return parts[1].strip() return None mapping_df = get_gene_mapping( annotation=gene_annotation, prob_col='ID', gene_col='gene_assignment' ) mapping_df['Gene'] = mapping_df['Gene'].apply(extract_first_gene_symbol) mapping_df = mapping_df.dropna() # 3. Apply gene mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Preview results print("\nFirst few rows and columns of gene expression data:") print(gene_data.iloc[:5, :5]) # 1. Normalize gene symbols and save gene data normalized_gene_data = normalize_gene_symbols_in_index(gene_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 selected_clinical_df = selected_clinical_df.rename(index={0: 'Longevity'}) linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, 'Longevity') # 4. Check for biased features and remove biased demographic ones is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Longevity') # 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="All subjects are male according to series summary. Age information not available." ) # 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)