# Path Configuration from tools.preprocess import * # Processing context trait = "Sjögrens_Syndrome" cohort = "GSE66795" # Input paths in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome" in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE66795" # Output paths out_data_file = "./output/preprocess/3/Sjögrens_Syndrome/GSE66795.csv" out_gene_data_file = "./output/preprocess/3/Sjögrens_Syndrome/gene_data/GSE66795.csv" out_clinical_data_file = "./output/preprocess/3/Sjögrens_Syndrome/clinical_data/GSE66795.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 # Based on the background info, this is a "whole genome microarray" study using whole blood samples, # so gene expression data is available is_gene_available = True # 2.1 Data Availability # trait_row=2: 'patient group' indicates pSS vs Control status trait_row = 2 # age_row: Age information is not available in sample characteristics age_row = None # gender_row=3: Gender information is available but shows only females gender_row = None # Set to None since it's a constant feature (all female) # 2.2 Data Type Conversion Functions def convert_trait(x): """Convert pSS status to binary: Control=0, Patient=1""" if not isinstance(x, str): return None x = x.lower().split(': ')[-1] if 'control' in x: return 0 elif 'patient' in x: return 1 return None # No age conversion function needed since age data not available convert_age = None # No gender conversion function needed since gender is constant convert_gender = None # 3. 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) ) # 4. Extract Clinical Features if trait_row is not None: # Extract features using the library function clinical_features = 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 preview = preview_df(clinical_features) print("Preview of clinical features:", preview) # Save to CSV clinical_features.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 gene identifiers start with "ILMN_" indicating these are Illumina probe IDs # These need to be mapped to official human 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)) # Extract probe ID and gene symbol mappings from annotation data mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # Apply mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Print preview of gene_data to verify conversion print("\nPreview of mapped gene data:") print(gene_data.shape) print(gene_data.head()) # 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)