# Path Configuration from tools.preprocess import * # Processing context trait = "Essential_Thrombocythemia" cohort = "GSE65161" # Input paths in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia" in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE65161" # Output paths out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE65161.csv" out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE65161.csv" out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE65161.csv" json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json" # Get relevant file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data from the matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Get dictionary of unique values per row in clinical data unique_values_dict = get_unique_values_by_row(clinical_data) # Print background info print("Background Information:") print("-" * 50) print(background_info) print("\n") # Print clinical data unique values print("Sample Characteristics:") print("-" * 50) for row, values in unique_values_dict.items(): print(f"{row}:") print(f" {values}") print() # 1. Gene Expression Data Availability is_gene_available = False # Based on the background info, this dataset seems to focus on molecular studies of cell lines rather than gene expression # 2.1 Data Availability trait_row = None # No suitable trait data (Essential Thrombocythemia) can be found - these are cell lines age_row = None # No age data available for cell lines gender_row = None # No gender data available for cell lines # 2.2 Data Type Conversion Functions def convert_trait(x): # Not needed since trait data is unavailable return None def convert_age(x): # Not needed since age data is unavailable return None def convert_gender(x): # Not needed since gender data is unavailable return None # 3. Save Initial 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 # trait_row is None ) # 4. Clinical Feature Extraction # Skip this step since trait_row is None