# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_cirrhosis" cohort = "GSE285291" # Input paths in_trait_dir = "../DATA/GEO/Liver_cirrhosis" in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE285291" # Output paths out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE285291.csv" out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE285291.csv" out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE285291.csv" json_path = "./output/preprocess/3/Liver_cirrhosis/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 check # From series title and summary, this is clearly a gene expression study focused on OXPHOS genes is_gene_available = True # 2.1 Data availability check # trait_row: status field indicates cirrhosis status (control vs compensated/decompensated) trait_row = 1 # age_row: age is not available in sample characteristics age_row = None # gender_row: no gender info in characteristics but summary states all are men gender_row = None # 2.2 Data type conversion functions def convert_trait(value: str) -> Optional[int]: """Convert cirrhosis status to binary""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if value == 'control': return 0 elif value in ['compensated', 'decompensated']: return 1 return None def convert_age(value: str) -> Optional[float]: """Convert age to float - not used since age not available""" return None def convert_gender(value: str) -> Optional[int]: """Convert gender to binary - not used since gender not available""" 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_data, 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 preview_result = preview_df(clinical_df) print(f"Preview of clinical features: {preview_result}") # Save clinical data os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) 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 identifiers are already in human gene symbol format (e.g. A2M, AADAT, ABL1) # No mapping needed since they follow standard HGNC gene nomenclature requires_gene_mapping = False # 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="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)