# Path Configuration from tools.preprocess import * # Processing context trait = "Lower_Grade_Glioma" cohort = "GSE74567" # Input paths in_trait_dir = "../DATA/GEO/Lower_Grade_Glioma" in_cohort_dir = "../DATA/GEO/Lower_Grade_Glioma/GSE74567" # Output paths out_data_file = "./output/preprocess/3/Lower_Grade_Glioma/GSE74567.csv" out_gene_data_file = "./output/preprocess/3/Lower_Grade_Glioma/gene_data/GSE74567.csv" out_clinical_data_file = "./output/preprocess/3/Lower_Grade_Glioma/clinical_data/GSE74567.csv" json_path = "./output/preprocess/3/Lower_Grade_Glioma/cohort_info.json" # Step 1: Get file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Step 2: Extract background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Step 3: Get dictionary of unique values for each clinical feature unique_values_dict = get_unique_values_by_row(clinical_data) # Step 4: Print background info and sample characteristics print("Dataset Background Information:") print("-" * 80) print(background_info) print("\nSample Characteristics:") print("-" * 80) print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability is_gene_available = True # Yes, this dataset contains gene expression data based on background # 2.1 Data Availability # The dataset contains cell line data (U251/U373) and experimental construct data # Not suitable for human trait studies trait_row = None # No human trait data available age_row = None # No age data gender_row = None # No gender data # 2.2 Data Type Conversion Functions def convert_trait(x): return None # Not needed since trait_row is None def convert_age(x): return None # Not needed since age_row is None def convert_gender(x): return None # Not needed since gender_row is 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 # Skip since trait_row is None (no clinical data available) # 1. Extract gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # 2. Print first 20 row IDs print("First 20 gene/probe identifiers:") print(genetic_data.index[:20]) requires_gene_mapping = True # 1. Extract gene annotation data from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # 2. Preview annotation data print("Column names and first few values in gene annotation data:") print(preview_df(gene_annotation)) # Preview additional rows to check for gene annotations print("\nPreview of rows 100-105:") print(preview_df(gene_annotation.iloc[100:105])) # 1. ID is in 'ID' column and gene symbol is in 'GENE_SYMBOL' column # 2. Get mapping between gene identifiers and gene symbols mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL') # 3. Convert probe data to gene expression data using mapping gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview the first few genes and their expression values print("First few genes in mapped expression data:") print(preview_df(gene_data)) # 1. Normalize gene symbols and save gene data normalized_gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_data.to_csv(out_gene_data_file) # Skip clinical data linking and missing value handling since no clinical data available # 2. Final validation and save metadata # Set is_biased=False since trait data doesn't exist to be biased 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=False, df=normalized_gene_data, note="Dataset contains cell line data (U251/U373), not suitable for human trait studies." ) # Skip saving linked data since not usable