# Path Configuration from tools.preprocess import * # Processing context trait = "Epilepsy" cohort = "GSE64123" # Input paths in_trait_dir = "../DATA/GEO/Epilepsy" in_cohort_dir = "../DATA/GEO/Epilepsy/GSE64123" # Output paths out_data_file = "./output/preprocess/3/Epilepsy/GSE64123.csv" out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE64123.csv" out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE64123.csv" json_path = "./output/preprocess/3/Epilepsy/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 is gene expression data from stem cells, not miRNA or methylation is_gene_available = True # 2.1 Data Availability # This is a drug testing dataset, so patients don't have epilepsy - trait data not available trait_row = None # Age and gender not recorded in sample characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x): # Not used since trait_row is None return None def convert_age(x): # Not used since age_row is None return None def convert_gender(x): # Not used since gender_row is None 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. Skip clinical feature extraction since trait_row is None # 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]) requires_gene_mapping = True # Extract gene annotation data, excluding control probe lines gene_metadata = get_gene_annotation(soft_file) # Preview filtered annotation data print("Column names:") print(gene_metadata.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_metadata)) # 1. Observe the data: # In gene expression data, IDs are like '100009676_at', '10000_at' # In gene annotation, 'ID' contains similar format like '1_at', '10_at' # 'Description' contains gene names/symbols # 2. Get gene mapping dataframe mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Description') # 3. Convert probe-level measurements to gene-level expression gene_data = apply_gene_mapping(genetic_df, mapping_df) # Preview results print("Gene expression data shape after mapping:", gene_data.shape) print("\nPreview of first few rows and columns:") print(gene_data.head().iloc[:, :5]) # 1. Normalize gene symbols and save gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # Create a dummy dataframe with just gene data since we lack trait data df = pd.DataFrame(index=gene_data.index) # Save metadata indicating dataset is not usable due to lack of trait data is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, # No trait data available is_biased=True, # Consider as biased since trait is missing entirely df=df, note="Drug testing dataset without trait data for epilepsy analysis" ) # No need to save linked data since dataset is not usable for trait analysis