# Path Configuration from tools.preprocess import * # Processing context trait = "Sarcoma" cohort = "GSE197147" # Input paths in_trait_dir = "../DATA/GEO/Sarcoma" in_cohort_dir = "../DATA/GEO/Sarcoma/GSE197147" # Output paths out_data_file = "./output/preprocess/3/Sarcoma/GSE197147.csv" out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE197147.csv" out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE197147.csv" json_path = "./output/preprocess/3/Sarcoma/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 mentioning "Gene expression profiling was performed", is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability # Extract from row 0, where histotype indicates tumor type trait_row = 0 # Age and gender not available in sample characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert histotype value to binary indicating if it's Sarcoma (RMS)""" # Extract value after colon and strip whitespace if ':' in value: value = value.split(':')[1].strip() # RMS (Rhabdomyosarcoma) is a type of sarcoma return 1 if value == 'RMS' else 0 convert_age = None convert_gender = None # 3. Save Metadata # trait_row is not None, so trait data is available validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=True ) # 4. Clinical Feature Extraction # Since trait_row is not None, extract clinical features 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 and save clinical data print("Clinical data preview:") print(preview_df(clinical_df)) # Save clinical data clinical_df.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]) # The identifiers appear to be from a microarray platform (TC* format) # and not standard human gene symbols, so they need to be mapped requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Preview the annotation data structure and check usability print("Column names:") print(gene_annotation.columns) print("\nFirst few rows preview:") print(preview_df(gene_annotation)) # The annotation data lacks a clear mapping between probe IDs and gene symbols # SPOT_ID.1 contains gene info but in a complex format with multiple transcript records # This makes reliable gene symbol mapping impossible print("\nWarning: Gene annotation structure not suitable for probe-to-gene symbol mapping") gene_annotation = None # Since we can't properly annotate genes for human analysis, # update metadata to indicate gene data is not available is_gene_available = False 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 )