# Path Configuration from tools.preprocess import * # Processing context trait = "Pheochromocytoma_and_Paraganglioma" cohort = "GSE19422" # Input paths in_trait_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma" in_cohort_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma/GSE19422" # Output paths out_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/GSE19422.csv" out_gene_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/gene_data/GSE19422.csv" out_clinical_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE19422.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 # Based on the background info mentioning "Gene expression profiling" and "cDNA microarray", # this dataset contains gene expression data is_gene_available = True # 2.1 Row identifiers for clinical features # tissue type indicates tumor/normal status (trait) trait_row = 0 # Age and gender are not available in sample characteristics age_row = None gender_row = None # 2.2 Data type conversion functions def convert_trait(value): """Convert tissue type to binary: 1 for tumor (PCC/PGL), 0 for normal""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if 'normal' in value: return 0 elif 'tumor' in value or 'pcc' in value or 'pgl' in value: return 1 return None convert_age = None convert_gender = None # 3. Save metadata about data availability 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 since trait data is available 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 ) # Preview the extracted features print("Preview of clinical features:") print(preview_df(clinical_features)) # Save clinical data 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]) # Based on the gene identifiers which start with "A_23_P", these are Agilent microarray probe IDs, not gene symbols # They need to be mapped to human gene symbols for analysis 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)) # Get gene mapping data # 'ID' in gene annotation matches probe IDs in expression data # 'GENE_SYMBOL' contains the target gene symbols mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL') # Convert probe measurements to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview results print("Shape of original probe data:", genetic_data.shape) print("Shape after mapping to genes:", gene_data.shape) print("\nFirst few rows and columns of gene expression data:") print(gene_data.head().iloc[:, :5]) # 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 = "Dataset contains gene expression data from adrenal tissue samples, with cases identified by presence of pheochromocytoma" 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)