# Path Configuration from tools.preprocess import * # Processing context trait = "Huntingtons_Disease" cohort = "GSE154141" # Input paths in_trait_dir = "../DATA/GEO/Huntingtons_Disease" in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE154141" # Output paths out_data_file = "./output/preprocess/3/Huntingtons_Disease/GSE154141.csv" out_gene_data_file = "./output/preprocess/3/Huntingtons_Disease/gene_data/GSE154141.csv" out_clinical_data_file = "./output/preprocess/3/Huntingtons_Disease/clinical_data/GSE154141.csv" json_path = "./output/preprocess/3/Huntingtons_Disease/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) # Get unique values for each clinical feature unique_values_dict = get_unique_values_by_row(clinical_data) # Print background information print("Background Information:") print(background_info) print("\nSample Characteristics:") print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability # Not likely to contain gene expression data - sample characteristics show this is a sorted cell/lentivirus experiment is_gene_available = False # 2. Clinical Feature Data # 2.1 Data Availability # Looking at sample characteristics, trait data can be inferred from lentivirus status (Q73 vs Q23) trait_row = 1 # Age and gender are not available age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): # Extract value after colon value = value.split(': ')[1].strip() # Q73 represents HD (1), Q23 is control (0), pTANK is None if value == 'Q73': return 1 elif value == 'Q23': return 0 return None def convert_age(value): return None def convert_gender(value): return None # 3. Save Metadata # trait_row is not None, so trait data is available 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 if trait_row is not None: clinical_df = geo_select_clinical_features( clinical_df=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 ) print("Preview of clinical data:") print(preview_df(clinical_df)) clinical_df.to_csv(out_clinical_data_file)