# Path Configuration from tools.preprocess import * # Processing context trait = "Mesothelioma" cohort = "GSE112154" # Input paths in_trait_dir = "../DATA/GEO/Mesothelioma" in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE112154" # Output paths out_data_file = "./output/preprocess/3/Mesothelioma/GSE112154.csv" out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE112154.csv" out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE112154.csv" json_path = "./output/preprocess/3/Mesothelioma/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("\nClinical Data Structure:") print(f"Shape: {clinical_data.shape}") print("\nFirst few rows:") print(clinical_data.head()) print("\nSample Characteristics:") # Set pandas to display all rows pd.set_option('display.max_rows', None) # 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) # Reset display options pd.reset_option('display.max_rows') # 1. Gene Expression Data Availability is_gene_available = True # Based on series title and design, this is gene expression profiling data # 2.1 Data Availability # trait can be inferred from sample type - normal vs tumor trait_row = 0 # age and gender not available in the data age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): if not isinstance(value, str): return None value = value.lower() if 'sample type:' not in value: return None value = value.split('sample type:')[1].strip() # Convert to binary: 0 for normal, 1 for disease (DMPM) if 'normal peritoneum' in value: return 0 elif 'dmpm' in value: return 1 return None def convert_age(value): return None # No age data def convert_gender(value): return None # No gender data # 3. Save initial filtering results is_usable = 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 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 ) # Preview the extracted features preview = preview_df(clinical_features) print("Clinical Features Preview:", preview) # Save to CSV 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]) # Review the gene identifiers in the gene expression data # The identifiers start with "ILMN_" which indicates these are Illumina probe IDs # These 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)) # Extract mapping between probe IDs and gene symbols gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # Apply gene mapping to convert probe data to gene expression data gene_data = apply_gene_mapping(genetic_data, gene_mapping) print("\nShape after mapping:", gene_data.shape) print("\nFirst few rows of gene expression data:") print(gene_data.head()) # 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(clinical_features, 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 healthy cells and mesothelioma cell lines, suitable for case-control analysis." 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)