# Path Configuration from tools.preprocess import * # Processing context trait = "Asthma" cohort = "GSE123086" # Input paths in_trait_dir = "../DATA/GEO/Asthma" in_cohort_dir = "../DATA/GEO/Asthma/GSE123086" # Output paths out_data_file = "./output/preprocess/3/Asthma/GSE123086.csv" out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123086.csv" out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123086.csv" json_path = "./output/preprocess/3/Asthma/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 dataset contains gene expression data from CD4+ T cells using microarray is_gene_available = True # 2.1 Data Availability # Trait is in Feature 1 (primary diagnosis), values include ASTHMA and others trait_row = 1 # Gender is in Feature 2 and 3 (Sex appears in both) gender_row = 2 # Age appears in Features 3 and 4 age_row = 3 # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert trait values to binary (0 for control, 1 for case)""" if not isinstance(value, str): return None value = value.split(': ')[-1].upper() if 'ASTHMA' in value: return 1 elif value == 'HEALTHY_CONTROL': return 0 return None def convert_age(value: str) -> float: """Convert age values to continuous numeric""" if not isinstance(value, str): return None if not value.startswith('age: '): return None try: return float(value.split(': ')[-1]) except: return None def convert_gender(value: str) -> int: """Convert gender values to binary (0 for female, 1 for male)""" if not isinstance(value, str): return None if not value.startswith('Sex: '): return None value = value.split(': ')[-1].upper() if value == 'FEMALE': return 0 elif value == 'MALE': 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. 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 ) print("Preview of extracted clinical features:") print(preview_df(clinical_features)) # Save clinical features clinical_features.to_csv(out_clinical_data_file) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # 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) requires_gene_mapping = True # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file) # Preview annotation dataframe structure print("Gene Annotation Preview:") print("Column names:", gene_annotation.columns.tolist()) print("\nFirst few rows as dictionary:") print(preview_df(gene_annotation)) # The IDs in gene annotation correspond to the row IDs in gene expression data # The ENTREZ_GENE_ID contains IDs that we first map to mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID') # Apply the mapping to convert probe expression to gene expression entrez_data = apply_gene_mapping(gene_data, mapping_df) # Convert Entrez IDs to gene symbols using NCBI synonym database gene_data = normalize_gene_symbols_in_index(entrez_data) # Save the gene expression data gene_data.to_csv(out_gene_data_file) # Load previously saved data gene_data = pd.read_csv(out_gene_data_file, index_col=0) clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) # Inspect data alignment print("Clinical data shape:", clinical_data.shape) print("Gene data shape:", gene_data.shape) print("Clinical data columns:", clinical_data.columns[:5]) print("Gene data columns:", gene_data.columns[:5]) # Transpose data to get samples in rows, genes in columns clinical_data = clinical_data.T gene_data = gene_data.T linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, 'Asthma') # 4. Evaluate bias is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Asthma') # 5. 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_biased, df=linked_data, note="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls." ) # 6. Save linked data if usable if is_usable: linked_data.to_csv(out_data_file) # Load previously saved data gene_data = pd.read_csv(out_gene_data_file, index_col=0) clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) # Verify data validity if gene_data.empty: print("Gene expression data is empty. Previous preprocessing steps likely failed.") is_gene_available = False is_trait_available = True validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=is_trait_available, is_biased=False, # Set a definite value df=clinical_data, # Provide the clinical data note="Gene expression data processing failed, resulting in empty data." ) else: # Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Evaluate bias is_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_biased, df=linked_data, note="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls." ) # Save linked data if usable if is_usable: linked_data.to_csv(out_data_file)