# Path Configuration from tools.preprocess import * # Processing context trait = "Cardiovascular_Disease" cohort = "GSE285666" # Input paths in_trait_dir = "../DATA/GEO/Cardiovascular_Disease" in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE285666" # Output paths out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE285666.csv" out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE285666.csv" out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE285666.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 # Yes, this dataset contains gene expression data from Affymetrix Human Exon arrays is_gene_available = True # 2.1 Data Availability # trait (Williams syndrome) is available in row 0 trait_row = 0 # Age and gender not available in characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): # Extract value after colon if ':' in value: value = value.split(':')[1].strip() # Convert to binary (0=control, 1=disease) if 'unaffected' in value.lower() or 'control' in value.lower(): return 0 elif 'williams syndrome' in value.lower() or 'ws' in value.lower(): return 1 return None def convert_age(value): return None def convert_gender(value): 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 if trait_row is not None: clinical_df = 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 the data preview = preview_df(clinical_df) print("Preview of clinical data:") print(preview) # Save clinical data clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_df = get_genetic_data(matrix_file) # Print first 20 row IDs print("First 20 gene/probe IDs:") print(list(genetic_df.index)[:20]) # These identifiers appear to be numeric probe IDs, not human gene symbols # Numeric probe IDs typically need to be mapped to gene symbols for biological interpretation requires_gene_mapping = True # Extract gene annotation data gene_metadata = get_gene_annotation(soft_file) # Preview column names and first few values print("Column names and preview of gene annotation data:") print(preview_df(gene_metadata)) # 1. Identify mapping columns: # ID column contains probe identifiers matching the gene expression data # gene_assignment contains gene symbols with additional information # 2. Get gene mapping dataframe mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # 3. Apply gene mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_df, mapping_df) # Preview result print("\nFirst 5 genes and their expression values:") print(preview_df(gene_data, n=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_df, 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 = "Clinical data structure: binary disease status (Canavan disease) with gender information. Gender distribution is biased with a significant imbalance." 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)