# Path Configuration from tools.preprocess import * # Processing context trait = "Sarcoma" cohort = "GSE133228" # Input paths in_trait_dir = "../DATA/GEO/Sarcoma" in_cohort_dir = "../DATA/GEO/Sarcoma/GSE133228" # Output paths out_data_file = "./output/preprocess/3/Sarcoma/GSE133228.csv" out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE133228.csv" out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE133228.csv" json_path = "./output/preprocess/3/Sarcoma/cohort_info.json" # Get file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) print("Background Information:") print(background_info) print("\nSample Characteristics:") # Get dictionary of unique values per row unique_values_dict = get_unique_values_by_row(clinical_data) for row, values in unique_values_dict.items(): print(f"\n{row}:") print(values) # 1. Gene Expression Data Availability # Based on the background info, this seems to be a SuperSeries focusing on molecular mechanisms # and doesn't directly contain gene expression data is_gene_available = False # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability trait_row = 2 # "tumor type" row age_row = 1 # "age" row gender_row = 0 # "gender" row # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert tumor type to binary""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() # All samples are primary tumor, convert to 1 if 'primary tumor' in value: return 1 return None def convert_age(value: str) -> float: """Convert age to continuous numeric value""" if not value or ':' not in value: return None try: age = float(value.split(':')[1].strip()) return age except: return None def convert_gender(value: str) -> int: """Convert gender to binary (0=female, 1=male)""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if value == 'female': return 0 elif value == 'male': return 1 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. Clinical Feature Extraction if trait_row is not None: clinical_df = geo_select_clinical_features(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 data preview = preview_df(clinical_df) print("Preview of processed clinical data:") print(preview) # Save to CSV clinical_df.to_csv(out_clinical_data_file) # Get gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Examine data structure print("Data structure and head:") print(genetic_data.head()) print("\nShape:", genetic_data.shape) print("\nFirst 20 row IDs (gene/probe identifiers):") print(list(genetic_data.index)[:20]) # Get a few column names to verify sample IDs print("\nFirst 5 column names:") print(list(genetic_data.columns)[:5]) # Review gene identifiers - appear to be custom probe IDs (ending in "_at") # rather than standard human gene symbols requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Preview column names and values from annotation dataframe print("Gene annotation DataFrame preview:") print(preview_df(gene_annotation)) # First, let's see all columns in the annotation data to find gene symbols print("All columns in gene annotation:") print(gene_annotation.columns) # Extract mapping between probe IDs and gene symbols # Use "GB_ACC" or "Gene Symbol" column if available, otherwise need to extract from Description mapping_df = gene_annotation[['ID', 'Description']].copy() mapping_df['Gene'] = mapping_df['Description'].str.extract(r'\((.*?)\)', expand=False) # Extract text in parentheses mapping_df = mapping_df[['ID', 'Gene']].dropna() # Convert probe measurements to gene expression values gene_data = apply_gene_mapping(genetic_data, mapping_df) # Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) # Preview results print("\nShape of gene expression data:", gene_data.shape) print("\nFirst few rows of gene expression data:") print(gene_data.head()) print("\nFirst few gene symbols:") print(list(gene_data.index)[:10]) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) print("Gene data shape after normalization:", gene_data.shape) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # Load clinical data previously processed selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) print("\nClinical data shape:", selected_clinical_df.shape) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) print("\nLinked data shape:", linked_data.shape) # 3. Handle missing values systematically if trait in linked_data.columns: linked_data = handle_missing_values(linked_data, trait) # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and information saving note = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait." 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=trait_biased, df=linked_data, note=note ) # 6. Save linked data only if usable and not biased if is_usable and not trait_biased: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file) else: # Handle case where clinical features were not properly extracted note = "Failed to extract clinical trait information from sample characteristics." validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=None, df=None, note=note )