# Path Configuration from tools.preprocess import * # Processing context trait = "Depression" cohort = "GSE208668" # Input paths in_trait_dir = "../DATA/GEO/Depression" in_cohort_dir = "../DATA/GEO/Depression/GSE208668" # Output paths out_data_file = "./output/preprocess/3/Depression/GSE208668.csv" out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE208668.csv" out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE208668.csv" json_path = "./output/preprocess/3/Depression/cohort_info.json" # Get paths to the SOFT and matrix files soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values for each feature (row) in clinical data unique_values_dict = get_unique_values_by_row(clinical_data) # Print background info print("=== Dataset Background Information ===") print(background_info) print("\n=== Sample Characteristics ===") print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability # From the background info, this dataset contains transcriptome data from PBMCs # However, it mentions raw data was lost, so gene expression data is not available is_gene_available = False # 2.1 Data Availability # Depression trait can be inferred from "history of depression" field (key 9) trait_row = 9 # Age is available in key 1 age_row = 1 # Gender is available in key 2 gender_row = 2 # 2.2 Data Type Conversion Functions def convert_trait(x): if not isinstance(x, str): return None x = x.lower().strip() if 'history of depression:' not in x: return None value = x.split(':')[1].strip() if value == 'yes': return 1 elif value == 'no': return 0 return None def convert_age(x): if not isinstance(x, str): return None if 'age:' not in x: return None try: return float(x.split(':')[1].strip()) except: return None def convert_gender(x): if not isinstance(x, str): return None if 'gender:' not in x: return None value = x.split(':')[1].strip().lower() if value == 'female': return 0 elif value == 'male': return 1 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=(trait_row is not None)) # 4. Clinical Feature Extraction # Since trait_row is not None, extract clinical features 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 and save clinical data print("Preview of clinical data:") print(preview_df(clinical_df)) clinical_df.to_csv(out_clinical_data_file)