# Path Configuration from tools.preprocess import * # Processing context trait = "Alopecia" cohort = "GSE81071" # Input paths in_trait_dir = "../DATA/GEO/Alopecia" in_cohort_dir = "../DATA/GEO/Alopecia/GSE81071" # Output paths out_data_file = "./output/preprocess/3/Alopecia/GSE81071.csv" out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/GSE81071.csv" out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/GSE81071.csv" json_path = "./output/preprocess/3/Alopecia/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # Yes, this is gene expression data from RNA as mentioned in Series_overall_design is_gene_available = True # 2.1 Data Availability trait_row = 1 # disease state indicates Alopecia status through DLE/sCLE which cause alopecia age_row = None # Age information not available gender_row = None # Gender information not available # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> Optional[int]: """Convert disease state to binary value: 1 for DLE/SCLE (presence of lupus with alopecia) 0 for healthy/normal (control) """ if not value or ':' not in value: return None value = value.split(':')[1].strip().lower() if value in ['dle', 'scle']: return 1 elif value in ['healthy', 'normal']: return 0 return None # Since age and gender data not available, their conversion functions not needed convert_age = None convert_gender = None # 3. Save Metadata is_trait_available = trait_row is not None is_initial = 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. Clinical Feature Extraction if trait_row is not None: 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:") print(preview) # Save to CSV clinical_features.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file gene_data = get_genetic_data(matrix_file) # Print first 20 row IDs and shape of data to help debug print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of data:") print(gene_data.head()) print("\nFirst 20 gene/probe identifiers:") print(gene_data.index[:20]) # Inspect a snippet of raw file to verify identifier format import gzip with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: lines = [] for i, line in enumerate(f): if "!series_matrix_table_begin" in line: # Get the next 5 lines after the marker for _ in range(5): lines.append(next(f).strip()) break print("\nFirst few lines after matrix marker in raw file:") for line in lines: print(line) # These identifiers (e.g. '100009613_at', '100009676_at') are Affymetrix probe IDs # They need to be mapped to human gene symbols for proper analysis requires_gene_mapping = True # Extract gene annotation from SOFT file and get meaningful data gene_annotation = get_gene_annotation(soft_file) # Examine all columns to identify gene symbol information print("Gene annotation shape:", gene_annotation.shape) print("\nAll column names:") print(gene_annotation.columns.tolist()) # Print a few complete rows to see all available information print("\nFirst few complete rows:") print(gene_annotation.head(3).to_string()) # Print out some useful statistics print("\nNumber of non-null values in each column:") print(gene_annotation.count()) # Since this printout may be needed for next steps print("\nNote: Gene mapping will need probe IDs and gene symbols") print("Currently found columns:") print("'ID' column: Contains probe identifiers") print("Will need to identify appropriate column for gene symbols") # Get probe ID to Entrez ID mapping from annotation data mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID') # Use NCBI's Entrez Gene IDs to map to gene symbols mapped_gene_data = apply_gene_mapping(gene_data, mapping_df) # Normalize gene symbols in the index to standardize and aggregate values gene_data = normalize_gene_symbols_in_index(mapped_gene_data) # Save processed gene expression data gene_data.to_csv(out_gene_data_file) # Reload clinical data from source background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Extract clinical features with the corrected trait conversion 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 ) # Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Determine if features are biased is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Validate and save cohort info 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=is_trait_biased, df=linked_data, note="Gene expression data successfully mapped and linked with clinical features" ) # Save linked data only if usable AND trait is not biased if is_usable and not is_trait_biased: linked_data.to_csv(out_data_file)