# Path Configuration from tools.preprocess import * # Processing context trait = "Mesothelioma" cohort = "GSE117668" # Input paths in_trait_dir = "../DATA/GEO/Mesothelioma" in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE117668" # Output paths out_data_file = "./output/preprocess/3/Mesothelioma/GSE117668.csv" out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE117668.csv" out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE117668.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("\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 # Yes, this is a microarray study of gene expression data is_gene_available = True # 2.1 Data Availability # For trait (mesothelioma status), available in row 1 (diagnosis) trait_row = 1 # Age and gender not available in sample characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> Optional[int]: """Convert diagnosis to binary: 1 for mesothelioma, 0 for healthy""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if 'mesothelioma' in value: return 1 elif 'healthy' in value: return 0 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 if trait_row is not None: selected_clinical = 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 data preview = preview_df(selected_clinical) print("Preview of clinical data:") print(preview) # Save to CSV selected_clinical.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]) # Looking at the gene identifiers, we see probe names like "100009613_at", "10000_at", etc # These are Affymetrix probe IDs, not standard human gene symbols # We need to map these probe IDs to gene symbols before further 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)) # 1. Identify mapping columns: # 'ID' in annotation matches probe IDs in expression data (e.g., "100009613_at") # 'Description' contains gene names/descriptions # 2. Get gene mapping dataframe # The ID column already matches between annotation and expression data mapping_data = gene_annotation[['ID', 'Description']].copy() # Since Description contains full gene names, extract just the gene symbols def extract_first_word(text): """Extract the first word before any special characters or spaces""" if isinstance(text, str): return text.split()[0] return None mapping_data['Gene'] = mapping_data['Description'].apply(extract_first_word) mapping_data = mapping_data[['ID', 'Gene']] # 3. Convert probe data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview results print("Gene mapping preview:") print(preview_df(mapping_data)) print("\nGene expression data preview:") print(preview_df(gene_data)) print("\nShape:", gene_data.shape) # 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, 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)