# Path Configuration from tools.preprocess import * # Processing context trait = "Height" cohort = "GSE181339" # Input paths in_trait_dir = "../DATA/GEO/Height" in_cohort_dir = "../DATA/GEO/Height/GSE181339" # Output paths out_data_file = "./output/preprocess/3/Height/GSE181339.csv" out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE181339.csv" out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE181339.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 # Background info mentions PBMC transcriptomics and microarray experiment # 2.1 Data availability trait_row = 1 # Height info can be inferred from BMI groups age_row = 2 # Age is in row 2 gender_row = 0 # Gender is in row 0 # 2.2 Data type conversion functions def convert_trait(x: str) -> Optional[float]: """Convert BMI group to binary (0 for normal weight, 1 for overweight/obese)""" if not x or not isinstance(x, str): return None group = x.split(': ')[-1].strip().upper() if 'NW' in group: # Normal weight return 0 elif 'OW' in group or 'OB' in group: # Overweight/obese return 1 return None def convert_age(x: str) -> Optional[float]: """Convert age string to float""" if not x or not isinstance(x, str): return None try: return float(x.split(': ')[-1]) except: return None def convert_gender(x: str) -> Optional[float]: """Convert gender to binary (0 for female, 1 for male)""" if not x or not isinstance(x, str): return None gender = x.split(': ')[-1].strip().lower() if 'woman' in gender or 'female' in gender: return 0 elif 'man' in gender or 'male' in gender: 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 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 ) preview_result = preview_df(clinical_df) print("Preview of clinical data:") print(preview_result) clinical_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()) # The row IDs 7, 8, 15, 18, 20 appear to be numerical values rather than gene symbols # These look like probe IDs or probe set IDs that would need to be mapped to gene symbols requires_gene_mapping = True # Extract gene annotation data from SOFT file gene_metadata = get_gene_annotation(soft_file_path) # Display information about the annotation data print("Column names:") print(gene_metadata.columns.tolist()) print("\nPreview of first few rows:") print(json.dumps(preview_df(gene_metadata), indent=2)) # Get gene mapping from annotation data # ID column in annotation matches the index in gene expression data # GENE_SYMBOL column contains the target gene symbols mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL') # Apply gene mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Save the gene expression data gene_data.to_csv(out_gene_data_file) # Preview results print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of gene expression data:") print(gene_data.head()) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Judge whether features are biased and remove biased demographic features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and save metadata note = "Dataset contains gene expression data from PBMCs and height measurements from 40 subjects" is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=True, 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)