# Path Configuration from tools.preprocess import * # Processing context trait = "Intellectual_Disability" cohort = "GSE59630" # Input paths in_trait_dir = "../DATA/GEO/Intellectual_Disability" in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE59630" # Output paths out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE59630.csv" out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE59630.csv" out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE59630.csv" json_path = "./output/preprocess/3/Intellectual_Disability/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, we can see this is a gene expression study analyzing transcriptome, so: is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability # From sample characteristics, we can find: trait_row = 2 # 'disease status' indicates DS vs Control age_row = 4 # Age data available gender_row = 3 # Sex data available # 2.2 Data Type Conversion Functions def convert_trait(x): """Convert disease status to binary (0: Control, 1: DS)""" if x is None: return None value = x.split(': ')[-1].strip() if value == 'CTL': return 0 elif value == 'DS': return 1 return None def convert_age(x): """Convert age to continuous numeric value in years""" if x is None: return None value = x.split(': ')[-1].strip().lower() # Extract number and unit try: num = float(''.join(filter(str.isdigit, value))) if 'wg' in value: # weeks of gestation return num/52 # convert to years elif 'mo' in value: # months return num/12 # convert to years elif 'yr' in value: # years return num return None except: return None def convert_gender(x): """Convert gender to binary (0: Female, 1: Male)""" if x is None: return None value = x.split(': ')[-1].strip() if value == 'F': return 0 elif value == 'M': return 1 return None # 3. Save Metadata 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) # 4. Clinical Feature Extraction if trait_row is not None: clinical_features = 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 extracted features preview = preview_df(clinical_features) print("Preview of clinical features:", preview) # Save to CSV clinical_features.to_csv(out_clinical_data_file) # Extract gene expression data from the matrix file genetic_data = get_genetic_data(matrix_file_path) # Print first 20 row IDs print("First 20 row IDs:") print(genetic_data.index[:20].tolist()) # These appear to be probe IDs from a microarray platform rather than gene symbols # They are numeric IDs which need to be mapped to human 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()) # Look at general data statistics print("\nData shape:", gene_metadata.shape) # Display non-NaN value counts for key gene identifier columns print("\nNumber of non-NaN values in key columns:") for col in ['ID', 'gene_assignment']: print(f"{col}: {gene_metadata[col].notna().sum()}") # Preview rows with actual gene information print("\nPreview of rows with gene information:") gene_rows = gene_metadata[gene_metadata['gene_assignment'].notna()].head() print(json.dumps(preview_df(gene_rows), indent=2)) # From the previous output, we can see: # - Gene identifiers are in the 'ID' column # - Gene symbols are in 'gene_assignment' column and need to be extracted mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Print information about the mapping result print("\nOriginal probes:", len(genetic_data)) print("Mapped genes:", len(gene_data)) print("\nPreview of first few genes and their expression values:") print(json.dumps(preview_df(gene_data), indent=2)) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # Get clinical features clinical_features = 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 ) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # Early exit if trait values are all NaN if linked_data[trait].isna().all(): is_biased = True linked_data = None else: # 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 pediatric AML samples, focusing on Down syndrome cases versus other AML types." 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)