# Path Configuration from tools.preprocess import * # Processing context trait = "Parkinsons_Disease" cohort = "GSE49126" # Input paths in_trait_dir = "../DATA/GEO/Parkinsons_Disease" in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE49126" # Output paths out_data_file = "./output/preprocess/3/Parkinsons_Disease/GSE49126.csv" out_gene_data_file = "./output/preprocess/3/Parkinsons_Disease/gene_data/GSE49126.csv" out_clinical_data_file = "./output/preprocess/3/Parkinsons_Disease/clinical_data/GSE49126.csv" json_path = "./output/preprocess/3/Parkinsons_Disease/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 # Yes - series uses Agilent expression microarrays on PBMC samples is_gene_available = True # 2.1 Data Availability # Trait (PD) data is in row 0, binary control vs PD trait_row = 0 # Age and gender not available in characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): """Convert PD status to binary (0=control, 1=PD)""" if not isinstance(value, str): return None value = value.lower().split(": ")[-1].strip() if "control" in value: return 0 elif "parkinson" in value: return 1 return None convert_age = None convert_gender = None # 3. Save metadata for 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 data is available # Create DataFrame from characteristics characteristics_data = {0: ['disease state: control', "disease state: Parkinson's disease"]} clinical_data = pd.DataFrame.from_dict(characteristics_data, orient='index') selected_clinical = geo_select_clinical_features(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 clinical data preview_df(selected_clinical) # Save clinical features os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) 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]) # The IDs are numeric values starting from 12, which are definitely not human gene symbols # They appear to be probe IDs that need to be mapped to gene symbols requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Display column names and preview data print("Column names:") print(gene_annotation.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_annotation)) # 1. Get gene mapping from ID to GENE_SYMBOL mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL') # 2. Apply gene mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview gene data print("Gene data preview:") print(gene_data.head()) print("\nGene data shape:", gene_data.shape) # Reload clinical data that was processed earlier selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(gene_data) genetic_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df.T, genetic_data) # Print column names to debug print("Columns in linked data:") print(linked_data.columns[:10]) # Show first 10 columns # 3. Handle missing values systematically # The trait column name needs to match what's in the data linked_data = handle_missing_values(linked_data, trait_col="PD") # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "PD") # 5. Final validation and information saving note = "Gene expression data from PBMC cells of PD patients and controls" 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 if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file) # Reload clinical data that was processed earlier selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(gene_data) genetic_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df.T, genetic_data) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, trait_col='PD') # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'PD') # 5. Final validation and information saving note = "Gene expression data from peripheral blood mononuclear cells (PBMC) of Parkinson's Disease patients and controls" 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 if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file) # 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) # Check gene expression data availability is_gene_available = True # Agilent expression microarrays indicate gene expression data # Find rows for trait, age and gender trait_row = 0 # Disease state is recorded in row 0 age_row = None # Age not available in sample characteristics gender_row = None # Gender not available in sample characteristics # Define conversion functions def convert_trait(value): if not isinstance(value, str): return None value = value.lower().split(': ')[-1] if "parkinson" in value: return 1 elif "control" in value: return 0 return None def convert_age(value): return None # Not used since age data not available def convert_gender(value): return None # Not used since gender data not available # Validate and save initial cohort info 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) ) # Extract clinical features since trait data is available 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 and save clinical data preview_df(clinical_df) clinical_df.to_csv(out_clinical_data_file)