# Path Configuration from tools.preprocess import * # Processing context trait = "Huntingtons_Disease" cohort = "GSE34201" # Input paths in_trait_dir = "../DATA/GEO/Huntingtons_Disease" in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE34201" # Output paths out_data_file = "./output/preprocess/3/Huntingtons_Disease/GSE34201.csv" out_gene_data_file = "./output/preprocess/3/Huntingtons_Disease/gene_data/GSE34201.csv" out_clinical_data_file = "./output/preprocess/3/Huntingtons_Disease/clinical_data/GSE34201.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)) # Gene expression availability check is_gene_available = True # Based on Series_summary, this is mRNA expression data # Variable availability check and data type conversion trait_row = 1 # "hd genotype" contains HD status gender_row = 3 # "gender" contains gender information age_row = None # Age information not available # Data type conversion functions def convert_trait(value: str) -> int: """Convert HD status to binary: 1 for HD, 0 for wild type""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if 'hd' in value: return 1 elif 'wild type' in value: return 0 return None def convert_gender(value: str) -> int: """Convert gender to binary: 1 for male, 0 for female""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if 'female' in value: return 0 elif 'male' in value: return 1 return None # 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) ) # Extract clinical features clinical_df = geo_select_clinical_features( clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait, gender_row=gender_row, convert_gender=convert_gender ) # Preview the processed clinical data print(preview_df(clinical_df)) # Save clinical data 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 first 20 row IDs print("First 20 row IDs:") print(genetic_data.index[:20].tolist()) # These are Illumina array probe IDs ('ILMN_' prefix), not 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)) # Identify the correct columns from gene annotation data # 'ID' column in gene_metadata matches the probe identifiers (ILMN_*) in genetic_data # 'Symbol' column contains the corresponding gene symbols mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Symbol') # Apply mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview the first few genes print("\nFirst 20 genes in mapped data:") print(gene_data.index[:20].tolist()) # Save gene data gene_data.to_csv(out_gene_data_file) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # Get clinical features clinical_features = geo_select_clinical_features( clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait, gender_row=gender_row, convert_gender=convert_gender ) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_features, 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 human embryonic stem cells and their neural stem cell progeny, comparing HD mutation carriers with wild type controls" 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)