# Path Configuration from tools.preprocess import * # Processing context trait = "Asthma" cohort = "GSE184382" # Input paths in_trait_dir = "../DATA/GEO/Asthma" in_cohort_dir = "../DATA/GEO/Asthma/GSE184382" # Output paths out_data_file = "./output/preprocess/3/Asthma/GSE184382.csv" out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE184382.csv" out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE184382.csv" json_path = "./output/preprocess/3/Asthma/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # Based on series title and summary, this dataset contains miRNA data rather than gene expression data is_gene_available = False # 2. Variable Availability and Data Type Conversion # 2.1 Check data availability from sample characteristics trait_row = None # Cannot reliably determine asthma status from AIT treatment alone age_row = None # Age data not available gender_row = None # Gender data not available # 2.2 Define conversion functions (though data not available in this case) def convert_trait(x): return None def convert_age(x): return None def convert_gender(x): return None # 3. Save metadata about dataset usability 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 # Skip this step since trait_row is None