# Path Configuration from tools.preprocess import * # Processing context trait = "Aniridia" cohort = "GSE137997" # Input paths in_trait_dir = "../DATA/GEO/Aniridia" in_cohort_dir = "../DATA/GEO/Aniridia/GSE137997" # Output paths out_data_file = "./output/preprocess/3/Aniridia/GSE137997.csv" out_gene_data_file = "./output/preprocess/3/Aniridia/gene_data/GSE137997.csv" out_clinical_data_file = "./output/preprocess/3/Aniridia/clinical_data/GSE137997.csv" json_path = "./output/preprocess/3/Aniridia/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # Based on the background info, this is an mRNA study, so gene expression data should be available is_gene_available = True # 2. Variable Availability and Data Type Conversion # Trait (Aniridia) can be inferred from disease status (AAK vs control) in Feature 2 trait_row = 2 def convert_trait(value): if not isinstance(value, str): return None value = value.split(': ')[-1].strip().lower() # AAK (aniridia-associated keratopathy) indicates aniridia if value == 'aak': return 1 elif value == 'healthy control': return 0 return None # Age is available in Feature 0 age_row = 0 def convert_age(value): if not isinstance(value, str): return None try: age = int(value.split(': ')[-1]) return age except: return None # Gender is available in Feature 1 gender_row = 1 def convert_gender(value): if not isinstance(value, str): return None value = value.split(': ')[-1].strip().lower() if value in ['f', 'w']: # 'w' likely means woman/weiblich(German) return 0 elif value == 'm': return 1 return None # 3. Save Metadata 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. 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:") print(preview) # Save to CSV clinical_features.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file gene_data = get_genetic_data(matrix_file) # Print first 20 row IDs and shape of data to help debug print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of data:") print(gene_data.head()) print("\nFirst 20 gene/probe identifiers:") print(gene_data.index[:20]) # Based on the identifiers having the format "hsa-miR-*" and "hsa-let-*", these are microRNA identifiers, # not standard human gene symbols. They need to be mapped to their target genes. requires_gene_mapping = True # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file) # Print findings about dataset nature print("Dataset Analysis:") print("-" * 50) print("This dataset contains miRNA expression data (hsa-miR-* identifiers)") print("Standard gene mapping is not applicable for miRNA data") print("The dataset cannot be used for gene-level analysis without miRNA target information") print("-" * 50) # Set requires_gene_mapping to False since we cannot map miRNAs to genes requires_gene_mapping = False # Set is_gene_available to False since we don't have gene expression data is_gene_available = False # Save updated metadata about dataset usability validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=True, note="Dataset contains miRNA expression data instead of gene expression data" )