# Path Configuration from tools.preprocess import * # Processing context trait = "Height" cohort = "GSE102130" # Input paths in_trait_dir = "../DATA/GEO/Height" in_cohort_dir = "../DATA/GEO/Height/GSE102130" # Output paths out_data_file = "./output/preprocess/3/Height/GSE102130.csv" out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE102130.csv" out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE102130.csv" json_path = "./output/preprocess/3/Height/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 is_gene_available = True # Contains scRNA-seq data according to series title and summary # 2. Variable Availability and Data Type Conversion # 2.1 Data Keys # None of Height, age or gender information is available in the sample characteristics trait_row = None age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x): if ':' in str(x): val = str(x).split(':')[1].strip() try: return float(val) except: return None return None def convert_age(x): if ':' in str(x): val = str(x).split(':')[1].strip() try: return float(val) except: return None return None def convert_gender(x): if ':' in str(x): val = str(x).split(':')[1].strip().lower() if 'female' in val or 'f' in val: return 0 elif 'male' in val or 'm' in val: return 1 return None # 3. Save Metadata 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. Clinical Feature Extraction is skipped since trait_row is None # Extract gene expression data from the matrix file try: # First check if we can read the file and print contents print("First few lines of the matrix file:") with gzip.open(matrix_file_path, 'rt', encoding='latin-1') as file: for i, line in enumerate(file): if i < 10: print(line.strip()) if i > 10: break except Exception as e: print(f"Error reading matrix file: {e}")