# Path Configuration from tools.preprocess import * # Processing context trait = "Cardiovascular_Disease" cohort = "GSE182600" # Input paths in_trait_dir = "../DATA/GEO/Cardiovascular_Disease" in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE182600" # Output paths out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE182600.csv" out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE182600.csv" out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE182600.csv" json_path = "./output/preprocess/3/Cardiovascular_Disease/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 is_gene_available = True # Contains genome-wide gene expression data per background info # 2. Variable Availability and Data Type Conversion # 2.1 Row identification trait_row = 3 # Outcome Success/Failure indicates cardiovascular disease severity age_row = 1 # Age data available gender_row = 2 # Gender F/M data available # 2.2 Conversion functions def convert_trait(value: str) -> int: """Convert outcome to binary: Failure=0, Success=1""" if not value or ':' not in value: return None val = value.split(':')[1].strip().lower() if val == 'success': return 1 elif val in ['failure', 'fail']: return 0 return None def convert_age(value: str) -> float: """Convert age to float""" if not value or ':' not in value: return None try: return float(value.split(':')[1].strip()) except: return None def convert_gender(value: str) -> int: """Convert gender to binary: F=0, M=1""" if not value or ':' not in value: return None val = value.split(':')[1].strip().upper() if val == 'F': return 0 elif val == 'M': 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=True) # trait_row is not None # 4. Clinical Feature Extraction clinical_features = 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 extracted features print("Preview of extracted clinical features:") print(preview_df(clinical_features)) # Save clinical features clinical_features.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_df = get_genetic_data(matrix_file) # Print DataFrame shape and first 20 row IDs print("DataFrame shape:", genetic_df.shape) print("\nFirst 20 row IDs:") print(genetic_df.index[:20]) print("\nPreview of first few rows and columns:") print(genetic_df.head().iloc[:, :5]) # These IDs start with "ILMN_", indicating they are Illumina probe IDs # Illumina probe IDs need to be mapped to standard gene symbols requires_gene_mapping = True # Extract gene annotation data, excluding control probe lines gene_metadata = get_gene_annotation(soft_file) # Additional filtering to exclude control probes gene_metadata = gene_metadata[gene_metadata['Species'] != 'ILMN Controls'] # Preview filtered annotation data print("Column names and preview of gene annotation data:") print(preview_df(gene_metadata)) # 1. Identify relevant columns for mapping # From preview, we can see probe IDs are in 'ID' column and gene symbols are in 'Symbol' column prob_col = 'ID' gene_col = 'Symbol' # 2. Get mapping between probe IDs and gene symbols mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col) # 3. Apply mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_df, mapping_df) # Preview result print("Shape of gene expression data:", gene_data.shape) print("\nPreview of mapped gene expression data:") print(gene_data.head().iloc[:, :5]) # 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_features, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check and handle biased features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and save cohort info note = "Gene expression data from PBMC of cardiogenic shock patients under ECMO treatment. Contains trait (Success/Failure), age, and gender information." 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=trait_biased, df=linked_data, note=note ) # 6. Save linked data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)