# Path Configuration from tools.preprocess import * # Processing context trait = "Endometrioid_Cancer" cohort = "GSE73614" # Input paths in_trait_dir = "../DATA/GEO/Endometrioid_Cancer" in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614" # Output paths out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE73614.csv" out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE73614.csv" out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE73614.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)) # 1. Gene Expression Data Availability # Based on the series summary mentioning "transcriptional profile" and "gene expression signatures", # this dataset appears to contain gene expression data is_gene_available = True # 2. Variable Availability and Data Type Conversion # We cannot reliably determine case/control status from tissue field, so trait data is not available trait_row = None age_row = None gender_row = None def convert_trait(value: str) -> Optional[int]: if value is None: return None val = value.split(": ")[-1].strip().lower() if "endometrioid" in val: return 1 elif val in ["healthy", "normal", "benign"]: return 0 return None def convert_age(value: str) -> Optional[float]: if value is None: return None val = value.split(": ")[-1].strip() try: return float(val) except: return None def convert_gender(value: str) -> Optional[int]: if value is None: return None val = value.split(": ")[-1].strip().lower() if val in ["female", "f"]: return 0 elif val in ["male", "m"]: return 1 return None # 3. Save Metadata # Initial filtering - trait data not available 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 # Skip since trait_row is None # 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]) # These appear to be Agilent probe IDs (e.g. A_23_P100001) rather than 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)) # 1. From the preview, we can see that 'ID' contains probe IDs like A_23_P100001 # and 'GENE_SYMBOL' contains human gene symbols # 2. Get mapping between probe IDs and gene symbols gene_mapping = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL') # 3. Apply mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_df, gene_mapping) # Preview results print("Gene expression data shape:", gene_data.shape) print("\nFirst few genes and samples:") 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) # Final validation with the gene expression data is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=True, # No trait data means biased for our purpose df=gene_data, note="Gene expression data available but no trait information could be extracted" )