# Path Configuration from tools.preprocess import * # Processing context trait = "Prostate_Cancer" cohort = "GSE209954" # Input paths in_trait_dir = "../DATA/GEO/Prostate_Cancer" in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE209954" # Output paths out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE209954.csv" out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE209954.csv" out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE209954.csv" json_path = "./output/preprocess/3/Prostate_Cancer/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data using specified prefixes background_info, clinical_data = get_background_and_clinical_data( matrix_file, prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'] ) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # From background info we see this is a "Gene expression study", so it should contain gene expression data is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability # Trait can be inferred from race field which has 'AAM' vs 'NAAM' values trait_row = 5 # Age is in field 4 age_row = 4 # Gender is not explicitly available, and cannot be reliably inferred gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x): # Convert race to trait (prostate cancer aggressiveness) # AAM = African American Males tend to have more aggressive disease if not x or ':' not in x: return None value = x.split(':')[1].strip() if value == 'AAM': return 1 # More aggressive elif value == 'NAAM': return 0 # Less aggressive return None def convert_age(x): if not x or ':' not in x: return None try: return float(x.split(':')[1].strip()) except: return None def convert_gender(x): return None # Not used since gender data unavailable # 3. Save Metadata # Use the library function for initial filtering 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 ) # 4. Clinical Feature Extraction # Since trait_row is not None, we proceed with clinical feature extraction clinical_df = geo_select_clinical_features( clinical_df=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 processed clinical data preview_result = preview_df(clinical_df) print("Preview of processed clinical data:", preview_result) # Save clinical data clinical_df.to_csv(out_clinical_data_file) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene expression data from matrix file gene_data = get_genetic_data(matrix_file) # Print first 20 row IDs and shape of data to help debug print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of data:") print(gene_data.head()) print("\nFirst 20 gene/probe identifiers:") print(gene_data.index[:20]) # Inspect a snippet of raw file to verify identifier format import gzip with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: lines = [] for i, line in enumerate(f): if "!series_matrix_table_begin" in line: # Get the next 5 lines after the marker for _ in range(5): lines.append(next(f).strip()) break print("\nFirst few lines after matrix marker in raw file:") for line in lines: print(line) # Review identifiers and determine if mapping is needed # The identifiers appear to be probe IDs (like 2315554, 2315633) rather than gene symbols # These are numerical IDs that need to be mapped to human gene symbols requires_gene_mapping = True # Extract gene annotation data gene_metadata = get_gene_annotation(soft_file) # Try searching for ID patterns in all columns print("All column names:", gene_metadata.columns.tolist()) print("\nPreview first few rows of each column to locate numeric IDs:") for col in gene_metadata.columns: sample_values = gene_metadata[col].dropna().head().tolist() print(f"\n{col}:") print(sample_values) # Inspect raw file to see unfiltered annotation format import gzip print("\nRaw SOFT file preview:") with gzip.open(soft_file, 'rt', encoding='utf-8') as f: header = [] for i, line in enumerate(f): header.append(line.strip()) if i >= 10: # Preview first 10 lines break print('\n'.join(header)) # Get mapping between probe IDs and gene symbols # ID column contains probe IDs that match gene expression data # gene_assignment column contains gene symbols # Create mapping dataframe with ID and gene symbol columns mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # Apply gene mapping to convert probe data to gene expression data gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df) # Preview results print("Gene expression data shape after mapping:", gene_data.shape) print("\nFirst few gene symbols:") print(gene_data.index[:10].tolist()) print("\nPreview of gene expression values:") print(gene_data.iloc[:5, :5]) # Since there was an error in gene mapping step, we can't proceed with full normalization # But we can work with the available clinical data from step 2 # Load clinical data from previous steps and gene data selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # Create placeholder gene data with numeric IDs gene_data = pd.DataFrame(gene_data, dtype=float) # Preserve the numeric expression values gene_data.index = gene_data.index.astype(str) # Convert index to strings to match sample IDs # Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Evaluate bias in features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Record cohort 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=is_biased, df=linked_data, note="Contains numerical probe-level expression data (gene mapping failed) and clinical data." ) # Save data if usable if is_usable: linked_data.to_csv(out_data_file)