# Path Configuration from tools.preprocess import * # Processing context trait = "Height" cohort = "GSE152073" # Input paths in_trait_dir = "../DATA/GEO/Height" in_cohort_dir = "../DATA/GEO/Height/GSE152073" # Output paths out_data_file = "./output/preprocess/3/Height/GSE152073.csv" out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE152073.csv" out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE152073.csv" json_path = "./output/preprocess/3/Height/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) # Get unique values for each clinical feature unique_values_dict = get_unique_values_by_row(clinical_data) # Print background information print("Background Information:") print(background_info) print("\nSample Characteristics:") print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability # From the background info, this is Affymetrix microarray data from blood samples is_gene_available = True # 2. Variable Availability and Data Type Conversion # Trait (Height) is in row 2 trait_row = 2 def convert_trait(x): if pd.isna(x): return None try: # Extract value after colon and convert to float return float(x.split(': ')[1]) except: return None # Age data is in row 1 age_row = 1 def convert_age(x): if pd.isna(x): return None try: # Extract value after colon and convert to float return float(x.split(': ')[1]) except: return None # Gender data is in row 0, but only contains 'female' # Since all subjects are female, gender is not a useful variable gender_row = None def convert_gender(x): if pd.isna(x): return None val = x.split(': ')[1].lower() if val == 'female': return 0 elif val == '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. Extract clinical features if trait_row is not None: # Extract and process clinical features 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 data preview = preview_df(selected_clinical) print("Preview of selected clinical features:") print(preview) # Save to CSV selected_clinical.to_csv(out_clinical_data_file) # Extract gene expression data from the matrix file genetic_data = get_genetic_data(matrix_file_path) genetic_data = pd.read_csv(matrix_file_path, compression='gzip', skiprows=skip_rows, sep='\t', comment='!', header=0, index_col=0) # Print information about the data structure print("First 20 row IDs:") print(genetic_data.index[:20].tolist()) print("\nShape of genetic data:", genetic_data.shape) print("\nColumn names:", genetic_data.columns.tolist()) # Extract gene expression data from the matrix file with gzip.open(matrix_file_path, 'rt') as file: for i, line in enumerate(file): if "!series_matrix_table_begin" in line: skip_rows = i + 1 break # Read the genetic data while preserving correct sample IDs genetic_data = pd.read_csv(matrix_file_path, compression='gzip', skiprows=skip_rows-1, sep='\t', comment='!', header=0, index_col=0) # Print information about the data structure print("First 20 row IDs:") print(genetic_data.index[:20].tolist()) print("\nShape of genetic data:", genetic_data.shape) print("\nFirst few rows with sample IDs:") print(genetic_data.head()) # These identifiers appear to be from a microarray platform (likely HG-U133_Plus_2.0) # and need to be mapped to standard gene symbols requires_gene_mapping = True # Extract gene annotation data from SOFT file gene_metadata = get_gene_annotation(soft_file_path) # Display information about the annotation data print("Column names:") print(gene_metadata.columns.tolist()) print("\nPreview of first few rows:") print(json.dumps(preview_df(gene_metadata), indent=2)) # Extract gene annotation data gene_metadata = get_gene_annotation(soft_file_path) # Get mapping between IDs in gene expression data and gene symbols mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) # Print shape info print("Shape of gene data before mapping:", genetic_data.shape) print("Shape of gene data after mapping:", gene_data.shape) # Display a preview print("\nFirst few rows after mapping:") print(gene_data.head()) # Save gene expression data gene_data.to_csv(out_gene_data_file) # Extract gene expression data from the matrix file with gzip.open(matrix_file_path, 'rt') as file: for i, line in enumerate(file): if "!series_matrix_table_begin" in line: skip_rows = i + 1 break genetic_data = pd.read_csv(matrix_file_path, compression='gzip', skiprows=skip_rows-1, sep='\t', comment='!', header=0, index_col=0) # Print information about the data structure print("First few rows of the genetic data:") print(genetic_data.head()) print("\nShape of genetic data:", genetic_data.shape) print("\nColumn names:", genetic_data.columns.tolist()) # Extract gene annotation data gene_metadata = get_gene_annotation(soft_file_path) # Based on the preview, 'ID' column has the same identifiers as gene expression data # 'gene_assignment' contains gene symbols in a structured format mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) # Print shape info print("Shape of gene data before mapping:", genetic_data.shape) print("Shape of gene data after mapping:", gene_data.shape) # Display a preview print("\nFirst few rows after mapping:") print(gene_data.head()) # Save gene expression data gene_data.to_csv(out_gene_data_file) # Extract gene annotation and expression data gene_metadata = get_gene_annotation(soft_file_path) mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # Get gene expression data with gzip.open(matrix_file_path, 'rt') as file: for i, line in enumerate(file): if "!series_matrix_table_begin" in line: skip_rows = i + 1 break genetic_data = pd.read_csv(matrix_file_path, compression='gzip', skiprows=skip_rows-1, sep='\t', comment='!', header=0, index_col=0) # Map probes to genes gene_data = apply_gene_mapping(genetic_data, mapping_data) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Judge whether features are biased and remove biased demographic features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and save metadata note = "Dataset contains gene expression data from PBMCs and height measurements from 40 subjects" 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 ) # 6. Save the linked data only if it's 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) # Get unique values for each clinical feature unique_values_dict = get_unique_values_by_row(clinical_data) # Print background information print("Background Information:") print(background_info) print("\nSample Characteristics:") print(json.dumps(unique_values_dict, indent=2)) # 1. Gene Expression Data Availability # Yes, this dataset contains gene expression data from microarray (Affymetrix) is_gene_available = True # 2.1 Data Availability trait_row = 2 # height data in row 2 age_row = 1 # age data in row 1 gender_row = None # All females, so not useful for association study # 2.2 Data Type Conversion Functions def convert_trait(x): if pd.isna(x): return None try: # Extract numeric value after colon and convert to float return float(x.split(': ')[1]) except: return None def convert_age(x): if pd.isna(x): return None try: # Extract numeric value after colon and convert to float return float(x.split(': ')[1]) except: return None def convert_gender(x): # Not needed since gender is constant 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: 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=None, convert_gender=None ) # Preview the extracted features preview = preview_df(selected_clinical) print("Preview of selected clinical features:") print(preview) # Save to CSV selected_clinical.to_csv(out_clinical_data_file)