# Path Configuration from tools.preprocess import * # Processing context trait = "Sjögrens_Syndrome" cohort = "GSE143153" # Input paths in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome" in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE143153" # Output paths out_data_file = "./output/preprocess/3/Sjögrens_Syndrome/GSE143153.csv" out_gene_data_file = "./output/preprocess/3/Sjögrens_Syndrome/gene_data/GSE143153.csv" out_clinical_data_file = "./output/preprocess/3/Sjögrens_Syndrome/clinical_data/GSE143153.csv" json_path = "./output/preprocess/3/Sjögrens_Syndrome/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 # From background info, it's Agilent Whole Human Genome microarray data is_gene_available = True # 2.1 Identify rows containing trait, age and gender data trait_row = 1 # aecg disease classification, categorical values age_row = 2 # age values gender_row = 3 # Sex values # 2.2 Data type conversion functions def convert_trait(value): """Convert trait data (Primary SS vs non-SS) to binary.""" if value is None: return None # Extract value after colon and strip whitespace val = value.split(':')[-1].strip() if val == 'Primary SS': return 1 elif val == 'non-SS': return 0 return None def convert_age(value): """Convert age to continuous numeric.""" if value is None: return None try: # Extract number after colon age = int(value.split(':')[-1].strip()) return age except: return None def convert_gender(value): """Convert gender to binary (F:0, M:1).""" if value is None: return None # Extract value after colon and strip whitespace val = value.split(':')[-1].strip() if val == 'F': return 0 elif val == 'M': return 1 return None # 3. Save metadata - initial filtering 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. Extract clinical features since trait_row is not None if trait_row is not None: selected_clinical = 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 data print(preview_df(selected_clinical)) # Save to CSV selected_clinical.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]) # Review gene identifiers # The identifiers appear to be row numbers rather than human gene symbols # This suggests these are probe IDs and need mapping to gene symbols requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Preview column names and values from annotation dataframe print("Gene annotation DataFrame preview:") print(preview_df(gene_annotation)) # 1. Identify relevant columns for mapping (ID -> Gene Symbol) # From gene annotation preview, 'ID' matches probe identifiers and 'GeneName' has gene symbols prob_col = 'ID' gene_col = 'GeneName' # 2. Get mapping dataframe mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col) # 3. Apply mapping to convert probe-level data to gene expression gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview result print("\nGene expression data preview:") print(preview_df(gene_data)) print("\nShape:", gene_data.shape) # 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)