# Path Configuration from tools.preprocess import * # Processing context trait = "Pheochromocytoma_and_Paraganglioma" cohort = "GSE39716" # Input paths in_trait_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma" in_cohort_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma/GSE39716" # Output paths out_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/GSE39716.csv" out_gene_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/gene_data/GSE39716.csv" out_clinical_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE39716.csv" json_path = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/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 # This dataset contains human HuGene1.0-ST Gene Chip data, which is gene expression data is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data rows from sample characteristics trait_row = 2 # tissue type indicates normal vs tumor age_row = 0 # age data is available gender_row = 1 # gender data is available # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert tissue type to binary: 0 for normal, 1 for tumor""" if not value or '--' in value: return None value = value.split(': ')[1].lower() if 'normal' in value: return 0 elif 'tumor' in value: return 1 return None def convert_age(value: str) -> float: """Convert age to float""" if not value or '--' in value: return None try: return float(value.split(': ')[1]) except: return None def convert_gender(value: str) -> int: """Convert gender to binary: 0 for female, 1 for male""" if not value or '--' in value: return None value = value.split(': ')[1].lower() if value == 'female': return 0 elif value == 'male': return 1 return None # 3. Save Metadata 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 if trait_row is not None: clinical_features = 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 ) print("Preview of extracted clinical features:") print(preview_df(clinical_features)) clinical_features.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]) # The gene identifiers are 7-digit IDs starting with '7892', which appear to be Illumina probe IDs # These are not standard human gene symbols and will need to be mapped requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Display column names and preview data print("Column names:") print(gene_annotation.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_annotation)) # From the output, we can see that 'ID' in the annotation matches the row IDs in expression data # 'gene_assignment' contains gene symbol information # Extract mapping between identifiers and gene symbols mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Save the processed gene expression data gene_data.to_csv(out_gene_data_file) # Preview gene data structure print("\nProcessed gene data preview:") print(preview_df(gene_data)) print("\nShape:", gene_data.shape) # Reload clinical data that was processed earlier selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(gene_data) genetic_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) # 3. Handle missing values systematically 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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements" 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 if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)