# Path Configuration from tools.preprocess import * # Processing context trait = "Essential_Thrombocythemia" cohort = "GSE103176" # Input paths in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia" in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE103176" # Output paths out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE103176.csv" out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE103176.csv" out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE103176.csv" json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json" # Get relevant file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data from the matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Get dictionary of unique values per row in clinical data unique_values_dict = get_unique_values_by_row(clinical_data) # Print background info print("Background Information:") print("-" * 50) print(background_info) print("\n") # Print clinical data unique values print("Sample Characteristics:") print("-" * 50) for row, values in unique_values_dict.items(): print(f"{row}:") print(f" {values}") print() # 1. Gene Expression Data Availability # Series title mentions "Gene... expression profiles", so gene data is available is_gene_available = True # 2. Variable Availability and Data Row Identification # 2.1 Data Type Selection and Data Row Identification # Trait (ET vs Control) can be found in row 3 under 'disease' trait_row = 3 # Age is not provided in the characteristics age_row = None # Gender is in row 1 under 'Sex' gender_row = 1 # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert disease status to binary (0: control, 1: ET)""" if pd.isna(value): return None value = value.split(': ')[-1].strip().lower() if 'et' in value: return 1 elif 'healthy control' in value: return 0 return None def convert_age(value: str) -> float: """Convert age to float - not used since age not available""" return None def convert_gender(value: str) -> int: """Convert gender to binary (0: female, 1: male)""" if pd.isna(value): return None value = value.split(': ')[-1].strip().lower() if value == 'f': return 0 elif value == 'm': return 1 return None # 3. Save Metadata 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 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 extracted features preview_result = preview_df(selected_clinical) print("Preview of extracted clinical features:") print(preview_result) # Save to CSV selected_clinical.to_csv(out_clinical_data_file) # Extract gene expression data genetic_data = get_genetic_data(matrix_file_path) # Print first 20 probe IDs print("First 20 probe IDs:") print(genetic_data.index[:20]) # The identifiers appear to be probe IDs from a microarray platform, not standard gene symbols # They need to be mapped to human gene symbols for analysis requires_gene_mapping = True # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # Preview column names and first few values preview_dict = preview_df(gene_annotation) print("Column names and preview values:") for col, values in preview_dict.items(): print(f"\n{col}:") print(values) # Get unique probe IDs from gene expression data to understand the format probe_examples = genetic_data.index[:5].tolist() # Extract the complete platform annotation table gene_annotation = get_gene_annotation(soft_file_path, prefixes=['!platform_table_begin', '!platform_table_end']) # Extract columns for mapping and rename them mapping_data = get_gene_mapping(gene_annotation, prob_col='ID_REF', gene_col='Gene Symbol') # Apply mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview the result print("\nExample probe IDs from expression data:") print(probe_examples) print("\nFirst 5 rows of mapping data:") print(mapping_data.head()) print("\nFirst 5 rows and 3 columns of mapped gene expression data:") print(gene_data.iloc[:5, :3]) # Get unique probe IDs from gene expression data to understand the format probe_examples = genetic_data.index[:5].tolist() # Extract the complete platform annotation table gene_annotation = get_gene_annotation(soft_file_path) print("\nRaw annotation data columns:") print(gene_annotation.columns.tolist()) # Based on column names in the raw data, we can see that probe IDs are in the 'ID' column # and gene symbols are in the 'Gene Symbol' column mapping_data = pd.DataFrame({ 'ID': gene_annotation['ID'], 'Gene': gene_annotation['Gene Symbol'] }) # Fix any NaN values that might cause mapping issues mapping_data = mapping_data.dropna() # Apply mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Save gene expression data gene_data.to_csv(out_gene_data_file) # Preview results print("\nExample probe IDs from expression data:") print(probe_examples) print("\nFirst 5 rows of mapping data:") print(mapping_data.head()) print("\nFirst 5 rows and 3 columns of mapped gene expression data:") print(gene_data.iloc[:5, :3]) # Check if genetic data is empty if genetic_data.empty: print("Gene expression data is empty - cannot proceed with linking and analysis") # Record failure in cohort info is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=False, is_trait_available=True, is_biased=None, df=None, note="Gene mapping failed - unable to match probe IDs between expression and annotation data" ) else: # 1. Normalize gene symbols and save normalized gene data normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) normalized_gene_data.to_csv(out_gene_data_file) # Read the processed clinical data file clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # Link clinical and genetic data using the normalized gene data linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data) # Handle missing values systematically linked_data = handle_missing_values(linked_data, trait) # Detect bias in trait and demographic features, remove biased demographic features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Validate data quality and save cohort info note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available." 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_biased, df=linked_data, note=note ) # Save linked data if usable if is_usable: linked_data.to_csv(out_data_file) else: print(f"Dataset {cohort} did not pass quality validation and will not be saved.")