# Path Configuration from tools.preprocess import * # Processing context trait = "Thymoma" cohort = "GSE131027" # Input paths in_trait_dir = "../DATA/GEO/Thymoma" in_cohort_dir = "../DATA/GEO/Thymoma/GSE131027" # Output paths out_data_file = "./output/preprocess/3/Thymoma/GSE131027.csv" out_gene_data_file = "./output/preprocess/3/Thymoma/gene_data/GSE131027.csv" out_clinical_data_file = "./output/preprocess/3/Thymoma/clinical_data/GSE131027.csv" json_path = "./output/preprocess/3/Thymoma/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 shape and first few rows to verify data print("Background Information:") print(background_info) print("\nClinical Data Shape:", clinical_data.shape) print("\nFirst few rows of Clinical Data:") print(clinical_data.head()) 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) # Gene expression data availability is_gene_available = True # Based on series summary mentioning expression features # Clinical data availability and conversion functions def convert_trait(value): if value is None: return None value = value.split(': ')[-1].strip() return 1 if value == 'Thymoma' else 0 def convert_age(value): # Age data not available in sample characteristics return None def convert_gender(value): # Gender data not available in sample characteristics return None # Find row index for trait data trait_row = 1 # Cancer type is in row 1 age_row = None # Age not available gender_row = None # Gender not available # Save initial 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) # Extract clinical features if trait data available if trait_row is not None: 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 extracted features print("Preview of extracted clinical features:") print(preview_df(clinical_df)) # Save clinical data clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Print first 20 row IDs print("First 20 gene/probe IDs:") print(list(genetic_data.index[:20])) # These appear to be Affymetrix probe IDs rather than human gene symbols # Affymetrix probes need to be mapped to official gene symbols requires_gene_mapping = True # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # Preview annotation structure preview = preview_df(gene_annotation) print("Gene annotation preview:") print(preview) # Get mapping between probe IDs and gene symbols mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') # Apply mapping to convert probe data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Print shape and preview first few gene symbols print("\nGene expression data shape after mapping:", gene_data.shape) print("\nFirst 10 gene symbols:") print(list(gene_data.index[:10])) # 1. Normalize gene symbols in gene expression data 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) print("\nGene data shape (normalized gene-level):", gene_data.shape) # Load clinical data previously processed selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) print("\nClinical data shape:", selected_clinical_df.shape) # 2. Link clinical and genetic data using normalized gene-level data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) print("\nLinked data shape:", linked_data.shape) # 3. Handle missing values systematically if trait in linked_data.columns: linked_data = handle_missing_values(linked_data, trait) # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and information saving note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database." 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=note ) # 6. Save linked data only if usable and not biased if is_usable and not trait_biased: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)