# Path Configuration from tools.preprocess import * # Processing context trait = "Height" cohort = "GSE97475" # Input paths in_trait_dir = "../DATA/GEO/Height" in_cohort_dir = "../DATA/GEO/Height/GSE97475" # Output paths out_data_file = "./output/preprocess/3/Height/GSE97475.csv" out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE97475.csv" out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE97475.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 # From title and background information, this appears to be a microarray study of blood PBMCs # Not pure miRNA or methylation data is_gene_available = True # 2. Variable Availability and Data Type Conversion # Height data is explicitly recorded in row 5 trait_row = 5 # Age data is recorded in row 81 age_row = 81 # Gender data can be inferred from row 118 gender_row = 118 def convert_trait(x): # Extract height value after colon if pd.isna(x) or ':' not in x: return None height = x.split(': ')[1] try: # Convert to float for continuous data return float(height) except: if height == 'NA': return None return None def convert_age(x): if pd.isna(x) or ':' not in x: return None age = x.split(': ')[1] try: # Convert to float for continuous data return float(age) except: if age == 'NA': return None return None def convert_gender(x): if pd.isna(x) or ':' not in x: return None gender = x.split(': ')[1] # Convert to binary (0=female, 1=male) if gender.lower() == 'female': return 0 elif gender.lower() == '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 if trait_row is not None: clinical_features_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) # Preview the extracted data print("Preview of extracted clinical features:") print(preview_df(clinical_features_df)) # Save to CSV clinical_features_df.to_csv(out_clinical_data_file) # Extract gene expression data from the matrix file genetic_data = get_genetic_data(matrix_file_path) # Print information about the data structure print("First few rows of the genetic data:") print(genetic_data.head()) print("\nShape of genetic data:", genetic_data.shape) print("\nColumn names:", genetic_data.columns.tolist()) # Looking at the gene identifiers in the index (A1BG, A26C3, A2LD1, etc.) # These appear to be standard HGNC gene symbols, so no mapping is needed requires_gene_mapping = False # Get gene expression data from matrix file gene_data = get_genetic_data(matrix_file_path) is_gene_available = len(gene_data.columns) > 1 # Load clinical data clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) # Check if there are any non-NaN height values is_trait_available = not clinical_data.loc[trait].isna().all() # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # 2-4. Skip data linking and processing if trait is not available linked_data = pd.DataFrame() is_biased = True if is_trait_available: linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) linked_data = handle_missing_values(linked_data, trait) is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and save metadata note = "This dataset contains valid gene expression data and demographic information (age and gender), but all height measurements are missing." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=is_trait_available, is_biased=is_biased, df=linked_data, note=note ) # 6. Save the linked data only if it's usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)