# Path Configuration from tools.preprocess import * # Processing context trait = "Metabolic_Rate" cohort = "GSE101492" # Input paths in_trait_dir = "../DATA/GEO/Metabolic_Rate" in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE101492" # Output paths out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE101492.csv" out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE101492.csv" out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE101492.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 # Based on background info, this study examines lncRNAs and gene expression in adipose tissue is_gene_available = True # 2. Variable Availability and Data Type Conversion # Trait (insulin sensitivity) is in row 3 trait_row = 3 def convert_trait(x): if not x or ':' not in x: return None value = x.split(':')[1].strip().lower() if 'resistant' in value: return 1 elif 'sensitive' in value: return 0 return None # Age is in row 2 age_row = 2 def convert_age(x): if not x or ':' not in x: return None try: return float(x.split(':')[1].strip()) except: return None # Gender is in row 1, but it's constant (all female) gender_row = None def convert_gender(x): return None # 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 the extracted features preview = preview_df(clinical_features) print("Preview of clinical features:", 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]) # Looking at the ID format (18670xxx), these appear to be probe IDs or Illumina IDs, # not standard HGNC gene symbols. Gene mapping will be required. 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 data with proper parsing of gene_assignment field relevant_rows = gene_annotation[~gene_annotation['gene_assignment'].str.contains('Housekeeping Controls', na=False)] def parse_gene_assignment(text): if pd.isna(text) or '---' in str(text): return None parts = str(text).split('//') if len(parts) >= 3: gene_info = parts[2].strip() if gene_info.startswith('gene:'): return gene_info.split(':')[1].strip() return gene_info return None relevant_rows['Gene'] = relevant_rows['gene_assignment'].apply(parse_gene_assignment) mapping_data = relevant_rows[['ID', 'Gene']].dropna() # Apply mapping to convert probe data to gene data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview the results print("Gene expression data shape after mapping:", gene_data.shape) print("\nFirst few gene symbols:") print(list(gene_data.index)[:10]) # 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)