# Path Configuration from tools.preprocess import * # Processing context trait = "Bladder_Cancer" cohort = "GSE203149" # Input paths in_trait_dir = "../DATA/GEO/Bladder_Cancer" in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE203149" # Output paths out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE203149.csv" out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE203149.csv" out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE203149.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 # Yes - the background info indicates gene expression data from microarray platform is_gene_available = True # 2.1 Data Availability # trait_row = 0 - Disease status shown in row 0, but only one value (all samples are cancer) # age - not available in sample characteristics # gender - not available in sample characteristics trait_row = None # Only one value, so not usable age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x): if pd.isna(x): return None value = str(x).split(': ')[-1].lower() if 'bladder cancer' in value: return 1 elif 'control' in value or 'normal' in value: return 0 return None def convert_age(x): if pd.isna(x): return None value = str(x).split(': ')[-1] try: return float(value) except: return None def convert_gender(x): if pd.isna(x): return None value = str(x).split(': ')[-1].lower() if 'female' in value or 'f' in value: return 0 elif 'male' in value or 'm' in value: return 1 return None # 3. Save Metadata # trait_row is None, so trait data not available is_trait_available = False if trait_row is None else True 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 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) 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)) # Extract gene identifiers and gene symbols and create mapping mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1') # Apply gene mapping to convert probe measurements to gene expression data gene_data = apply_gene_mapping(gene_data, mapping_data) # Normalize gene symbols in the gene expression data gene_data = normalize_gene_symbols_in_index(gene_data) # Save preprocessed gene expression data os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # 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) # 2. Create minimal clinical data (just sample IDs) and link with gene data sample_ids = pd.DataFrame(index=gene_data.columns) linked_data = pd.concat([sample_ids, gene_data.T], axis=1) # 3. Handle missing values systematically # Since we only have gene data, we only need to handle missing gene values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features is_biased = True # No trait data means the dataset is biased by definition # 5. Validate data quality and save metadata is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=is_biased, df=linked_data, note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis." ) # 6. Since is_biased is True, the data is not usable, so we don't save it # 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) # 2-6. Skip clinical linking since no trait data available is_biased = True # No trait data means biased by definition is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=is_biased, df=gene_data.T, note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis." )