# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_cirrhosis" cohort = "GSE150734" # Input paths in_trait_dir = "../DATA/GEO/Liver_cirrhosis" in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE150734" # Output paths out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE150734.csv" out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE150734.csv" out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE150734.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 # Based on series title and design mentioning "Gene expression profiling", this dataset contains gene expression data is_gene_available = True # 2. Variable Availability and Data Type Conversion # Fibrosis stage (key 0) indicates cirrhosis status trait_row = 0 def convert_trait(value: str) -> Optional[float]: if not value or ':' not in value: return None stage = value.split(':')[1].strip() # Convert fibrosis stage to binary (1 for cirrhosis) # Stage ≥ 4 typically indicates cirrhosis, but this dataset only has stages 0-1 return 0.0 # All samples are non-cirrhotic # Age and gender not available in characteristics age_row = None gender_row = None convert_age = None convert_gender = None # 3. Save metadata # is_trait_available=True since 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=True) # 4. Clinical Feature Extraction clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait) print("Preview of clinical features:") print(preview_df(clinical_features)) clinical_features.to_csv(out_clinical_data_file)