# Path Configuration from tools.preprocess import * # Processing context trait = "Telomere_Length" cohort = "GSE52237" # Input paths in_trait_dir = "../DATA/GEO/Telomere_Length" in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE52237" # Output paths out_data_file = "./output/preprocess/3/Telomere_Length/GSE52237.csv" out_gene_data_file = "./output/preprocess/3/Telomere_Length/gene_data/GSE52237.csv" out_clinical_data_file = "./output/preprocess/3/Telomere_Length/clinical_data/GSE52237.csv" json_path = "./output/preprocess/3/Telomere_Length/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) # 1. Gene Expression Data Availability # Based on background info, this is a study of gene expression and telomere length in airway epithelium is_gene_available = True # 2.1 Data Availability # Smoking status can be used as trait since it's binary and complete 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): if pd.isna(value): return None # Extract value after colon and strip whitespace value = value.split(':')[1].strip() # Convert smoking status to binary (0=nonsmoker, 1=smoker) if value == 'nonsmoker': return 0 elif value in ['smoker', 'S']: return 1 return None def convert_age(value): # Not needed as age data unavailable return None def convert_gender(value): # Not needed as gender data unavailable 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=True) # 4. Extract Clinical Features # Use the geo_select_clinical_features function 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 the extracted features print("Preview of clinical features:") 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]) # Review the gene identifiers # The IDs like "1007_s_at", "1053_at", etc. are Affymetrix probe IDs # from the HG-U133 array, not standard human gene symbols # They need to be mapped to gene symbols for analysis 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) # From the preview, 'ID' column matches the probe IDs in expression data # 'Gene Symbol' column contains the gene symbols to map to mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol') # Convert probe-level measurements to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Preview the converted gene data print("Gene expression data preview:") print(gene_data.head()) print("\nShape:", gene_data.shape) # Save the gene data gene_data.to_csv(out_gene_data_file) # 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)