# Path Configuration from tools.preprocess import * # Processing context trait = "Bladder_Cancer" cohort = "GSE138118" # Input paths in_trait_dir = "../DATA/GEO/Bladder_Cancer" in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE138118" # Output paths out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE138118.csv" out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE138118.csv" out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE138118.csv" json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # 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 is_gene_available = True # Based on the series title and summary, this is gene expression data from blood # 2.1 Data Availability trait_row = 0 # "stage at sample" contains cancer status age_row = 1 # Age information is available gender_row = None # Gender information is not available in the characteristics # 2.2 Data Type Conversion Functions def convert_trait(x): if pd.isna(x): return None value = x.split(': ')[-1].strip() # Convert to binary: Healthy=0, Any cancer stage=1 if value == 'Healthy': return 0 elif value in ['G1', 'G2', 'G3', 'G1 pTa', 'G2 pTa']: return 1 return None def convert_age(x): if pd.isna(x): return None value = x.split(': ')[-1].strip() try: # Convert to continuous numeric value return float(value) except: return None def convert_gender(x): return None # Not used since gender data is not available # 3. Save Initial Filtering Results 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. Extract Clinical Features 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 ) # Preview the processed clinical data preview_result = preview_df(clinical_df) print("Preview of processed clinical data:") print(preview_result) # Save clinical data os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) clinical_df.to_csv(out_clinical_data_file) # 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) # Observe the gene identifiers # The identifiers are numeric strings starting with '16650', which appears to be probe IDs # These are not standard human gene symbols and will need to be mapped requires_gene_mapping = True # Get file paths using library function soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file) # Preview gene annotation data print("Gene annotation columns and example values:") print(preview_df(gene_annotation)) # Get gene mapping data from annotation # For this dataset: # ID column contains probe IDs matching gene expression data # gene_assignment column contains gene symbols mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') # Apply gene mapping to convert probe expression to gene expression # The apply_gene_mapping function handles many-to-many mappings gene_data = apply_gene_mapping(gene_data, mapping_df) # Preview data shape and first few rows print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows:") print(gene_data.head()) # 1. Normalize gene symbols and save normalized gene data gene_data.index = gene_data.index.str.replace('-mRNA', '') 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) # Load previously saved clinical data clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # 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 for biased features and remove them if needed is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Validate and save cohort info 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="NanoString nCounter RNA profiling data for bladder cancer recurrence study" ) # 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)