# Path Configuration from tools.preprocess import * # Processing context trait = "Osteoporosis" cohort = "GSE62589" # Input paths in_trait_dir = "../DATA/GEO/Osteoporosis" in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE62589" # Output paths out_data_file = "./output/preprocess/3/Osteoporosis/GSE62589.csv" out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE62589.csv" out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE62589.csv" json_path = "./output/preprocess/3/Osteoporosis/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) print("Background Information:") print(background_info) print("\nSample Characteristics:") # Get dictionary of unique values per row unique_values_dict = get_unique_values_by_row(clinical_data) for row, values in unique_values_dict.items(): print(f"\n{row}:") print(values) # Gene expression data availability check # This is a SuperSeries and we don't have clear information about data type is_gene_available = False # Variable availability check # Trait row: Not directly available in characteristics, can't be inferred trait_row = None # Age row: Not available in characteristics age_row = None # Gender row: Available in row 2, but all female so not useful gender_row = None # Type conversion functions def convert_trait(x): """Convert trait status to binary""" if x is None: return None x = str(x).lower() if ':' in x: x = x.split(':')[1].strip() if 'osteoporosis' in x: return 1 elif 'control' in x or 'normal' in x: return 0 return None def convert_age(x): """Convert age to float""" if x is None: return None try: if ':' in x: x = x.split(':')[1].strip() return float(x) except: return None def convert_gender(x): """Convert gender to binary""" if x is None: return None x = str(x).lower() if ':' in x: x = x.split(':')[1].strip() if 'female' in x: return 0 elif 'male' in x: return 1 return None # 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) # Get gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Examine data structure print("Data structure and head:") print(genetic_data.head()) print("\nShape:", genetic_data.shape) print("\nFirst 20 row IDs (gene/probe identifiers):") print(list(genetic_data.index)[:20]) # Get a few column names to verify sample IDs print("\nFirst 5 column names:") print(list(genetic_data.columns)[:5]) # The row IDs appear to be probe IDs (numeric identifiers) rather than human gene symbols # These will need to be mapped to standard gene symbols for analysis requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Display column names and preview data print("Column names:") print(gene_annotation.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_annotation)) # From examining previous outputs, we see: # - Gene expression data uses numeric IDs in the 'ID' column # - Gene annotation has 'ID' column with matching IDs and 'gene_assignment' with gene symbols # Get gene mapping between probe IDs and gene symbols gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment') # Apply mapping to convert probe-level data to gene-level expression gene_data = apply_gene_mapping(genetic_data, gene_mapping) # Preview results print("\nShape of gene expression data after mapping:", gene_data.shape) print("\nFirst few rows of mapped gene expression data:") print(gene_data.head()) # Create minimal dummy DataFrame dummy_df = pd.DataFrame({trait: [0]}) # One row with trait value is_biased = True # Mark as biased/unusable note = "This is a SuperSeries without clear data type information. No clinical trait data available." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=False, is_trait_available=False, is_biased=is_biased, df=dummy_df, note=note )