# Path Configuration from tools.preprocess import * # Processing context trait = "Sickle_Cell_Anemia" cohort = "GSE41575" # Input paths in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia" in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE41575" # Output paths out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE41575.csv" out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE41575.csv" out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE41575.csv" json_path = "./output/preprocess/3/Sickle_Cell_Anemia/cohort_info.json" # Get file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) print("Background Information:") print(background_info) print("\nSample Characteristics:") # Get dictionary of unique values per row unique_values_dict = get_unique_values_by_row(clinical_data) for row, values in unique_values_dict.items(): print(f"\n{row}:") print(values) # 1. Gene Expression Data Availability # This is microRNA data, not gene expression data is_gene_available = False # 2.1 Data Availability # No trait status data - only control vs miR overexpression trait_row = None # No age data available age_row = None # No gender data available gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x): # Not needed since trait data not available return None def convert_age(x): # Not needed since age data not available return None def convert_gender(x): # Not needed since gender data 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=False) # 4. Skip clinical feature extraction since trait_row is None