# Path Configuration from tools.preprocess import * # Processing context trait = "Endometrioid_Cancer" cohort = "GSE94524" # Input paths in_trait_dir = "../DATA/GEO/Endometrioid_Cancer" in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524" # Output paths out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE94524.csv" out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE94524.csv" out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE94524.csv" json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json" # Get paths to the SOFT and matrix files soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values for each feature (row) in clinical data unique_values_dict = get_unique_values_by_row(clinical_data) # Print background info print("=== Dataset Background Information ===") print(background_info) print("\n=== Sample Characteristics ===") print(json.dumps(unique_values_dict, indent=2)) # Step 1: Gene Expression Data Availability # From title, this appears to be a gene expression dataset studying tamoxifen-associated endometrial tumors is_gene_available = True # Step 2: Variable Availability and Data Type Conversion # From characteristics dict, all samples are endometrioid adenocarcinoma (trait=1) trait_row = 0 age_row = None # Age data not available gender_row = None # Gender data not available, but since endometrial cancer, we know all patients are female def convert_trait(value: str) -> int: """Convert trait value to binary (0 for normal/control, 1 for endometrioid cancer)""" if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if 'endometrioid' in value and 'adenocarcinoma' in value: return 1 return None # Age conversion function not needed since age data unavailable convert_age = None # Gender conversion function not needed since gender data unavailable convert_gender = None # Step 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 ) # Step 4: Clinical Feature Extraction if trait_row is not None: selected_clinical_df = 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 selected features preview = preview_df(selected_clinical_df) print("Preview of selected clinical features:") print(preview) # Save clinical data selected_clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_df = get_genetic_data(matrix_file) # Print DataFrame shape and first 20 row IDs print("DataFrame shape:", genetic_df.shape) print("\nFirst 20 row IDs:") print(genetic_df.index[:20]) print("\nPreview of first few rows and columns:") print(genetic_df.head().iloc[:, :5]) # The gene identifiers appear to be just row numbers (1, 2, 3, etc.) # This indicates they need to be mapped to actual human gene symbols requires_gene_mapping = True # Extract gene annotation data, excluding control probe lines gene_metadata = get_gene_annotation(soft_file) # Preview filtered annotation data print("Column names:") print(gene_metadata.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_metadata)) # From the preview, we can see that 'ID' column matches the gene expression row IDs, # and 'HUGO' column contains the gene symbols mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='HUGO') # Apply gene mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_df, mapping_data) # Preview the mapped gene expression data print("Gene expression data shape after mapping:", gene_data.shape) print("\nFirst few gene symbols:") print(gene_data.index[:10]) print("\nPreview of first few rows and columns:") print(gene_data.head().iloc[:, :5]) # 1. Normalize gene symbols and save gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and metadata saving 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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines" ) # 6. Save linked data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)