# Path Configuration from tools.preprocess import * # Processing context trait = "Breast_Cancer" cohort = "GSE283522" # Input paths in_trait_dir = "../DATA/GEO/Breast_Cancer" in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE283522" # Output paths out_data_file = "./output/preprocess/3/Breast_Cancer/GSE283522.csv" out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE283522.csv" out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE283522.csv" json_path = "./output/preprocess/3/Breast_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") # Gene Expression Data Availability # Based on background info, this is RNA-seq data of breast cancer is_gene_available = True # Variable Availability and Data Type Conversion # Sample category indicates if sample is tumor or not trait_row = 6 # Age data available in 5-year ranges age_row = 2 # Sex is recorded explicitly gender_row = 5 def convert_trait(value: str) -> int: # Sample category field contains information about tumor status if value is None or pd.isna(value): return None value = value.lower() if 'invasive breast cancer' in value: return 1 elif 'true healthy' in value or 'no tumor' in value: return 0 return None def convert_age(value: str) -> float: if value is None or pd.isna(value) or value.endswith('not applicable'): return None # Extract age range and take the midpoint parts = value.replace('age: ', '').split(' - ') if len(parts) != 2: return None try: start = float(parts[0]) end = float(parts[1]) return (start + end) / 2 except: return None def convert_gender(value: str) -> int: if value is None or pd.isna(value): return None value = value.lower() if 'female' in value: return 0 elif 'male' in value: return 1 return None # Initial filtering and metadata saving 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) # Extract clinical features if trait data available if trait_row is not None: selected_clinical_df = geo_select_clinical_features( clinical_data, trait="Breast_Cancer", trait_row=trait_row, convert_trait=convert_trait, age_row=age_row, convert_age=convert_age, gender_row=gender_row, convert_gender=convert_gender ) print("Preview of extracted clinical data:") print(preview_df(selected_clinical_df)) selected_clinical_df.to_csv(out_clinical_data_file) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Debug marker line and data format with gzip.open(matrix_file, 'rt') as f: print("First 10 lines after finding marker:") for i, line in enumerate(f): if "!series_matrix_table_begin" in line: # Print next 10 lines after marker for j in range(10): try: next_line = next(f) print(f"Line {j+1}: {next_line[:200]}") except StopIteration: break break # Try reading gene expression data with modified settings gene_data = pd.read_csv(matrix_file, compression='gzip', skiprows=206, sep='\t', index_col=0) print("\nShape 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]) # Save gene expression data gene_data.to_csv(out_gene_data_file) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene expression data using the provided function gene_data = get_genetic_data(matrix_file) # Print information about the data 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]) # Save gene expression data gene_data.to_csv(out_gene_data_file) # After inspecting file format, we see this is RNA-seq data without probe annotations # Print finding and proceed with an empty annotation dataframe print("This is RNA-seq data where genes are directly measured without probes.") print("Gene annotation mapping step will be skipped.") # Create empty annotation dataframe to maintain pipeline compatibility gene_metadata = pd.DataFrame(columns=['ID', 'Gene']) print("\nEmpty annotation dataframe created with columns:") print(gene_metadata.columns.tolist()) # Record failure status validate_and_save_cohort_info( is_final=False, # Use initial filtering to record availability status cohort=cohort, info_path=json_path, is_gene_available=False, is_trait_available=False )