# Path Configuration from tools.preprocess import * # Processing context trait = "Metabolic_Rate" cohort = "GSE23025" # Input paths in_trait_dir = "../DATA/GEO/Metabolic_Rate" in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE23025" # Output paths out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE23025.csv" out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE23025.csv" out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE23025.csv" json_path = "./output/preprocess/3/Metabolic_Rate/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 dataset contains gene expression data from Affymetrix HG U133 plus 2.0 Arrays is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability trait_row = 3 # patient status indicates metabolic rate (case vs control) age_row = None # age data not available gender_row = None # gender data not available # 2.2 Data Type Conversion def convert_trait(x): """Convert case/control status to binary""" if x is None: return None val = x.split(': ')[-1].strip().lower() if val == 'case': return 1 elif val == 'control': return 0 return None convert_age = None # No age data convert_gender = None # No gender data # 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_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_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]) # Based on the gene identifiers in the format "XXXX_at" or "XXXX_s_at", # these appear to be Affymetrix probe IDs rather than human gene symbols 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 dataframe using ID and Gene Symbol columns gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') # Apply gene mapping to convert probe measurements to gene expression gene_data = apply_gene_mapping(genetic_data, gene_mapping) # Preview the converted gene data print("Gene expression data shape after mapping:", gene_data.shape) print("\nFirst few genes and their expression values:") print(gene_data.head()) # 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)