# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_cirrhosis" cohort = "GSE182065" # Input paths in_trait_dir = "../DATA/GEO/Liver_cirrhosis" in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE182065" # Output paths out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE182065.csv" out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE182065.csv" out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE182065.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 # The Series title and summary indicate gene expression profiling was performed is_gene_available = True # 2. Variable Analysis and Conversion Functions # 2.1 Data Availability: # - Trait (liver cirrhosis/fibrosis): Can be inferred from sample group (row 1) # - Age: Not available # - Gender: Not available trait_row = 1 age_row = None gender_row = None # 2.2 Conversion Functions def convert_trait(value: str) -> Optional[int]: """Convert sample group info to binary trait value""" if not isinstance(value, str): return None value = value.lower().split(": ")[-1] # Baseline samples are from fibrotic liver tissues per background info if "baseline" in value: return 1 # Vehicle control samples are also fibrotic tissues elif "vehicle control" in value: return 1 # Compound treatment samples are also fibrotic tissues elif "compound treatment" in value: return 1 return None def convert_age(value: str) -> Optional[float]: """Convert age info to float years""" return None def convert_gender(value: str) -> Optional[int]: """Convert gender info to binary""" return None # 3. Save initial metadata is_trait_available = 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=is_trait_available) # 4. Extract clinical features since trait_row is not None 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 the processed clinical data preview_dict = preview_df(clinical_df) print("Preview of processed clinical data:") print(preview_dict) # Save clinical data os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) clinical_df.to_csv(out_clinical_data_file)