# Path Configuration from tools.preprocess import * # Processing context trait = "Kidney_Clear_Cell_Carcinoma" cohort = "GSE117230" # Input paths in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma" in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE117230" # Output paths out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE117230.csv" out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE117230.csv" out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE117230.csv" json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/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 # Yes, this dataset contains transcriptional profiling data per background info is_gene_available = True # 2. Variable Analysis # 2.1 Data Availability # Trait: disease state from row 0 distinguishes ccRCC patients vs healthy controls trait_row = 0 # Age is not available in sample characteristics age_row = None # Gender is not available in sample characteristics gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert disease state to binary: 0 for healthy control, 1 for ccRCC""" if not isinstance(value, str): return None value = value.split(': ')[-1].lower() if 'ccrcc patient' in value: return 1 elif 'healthy control' in value: return 0 return None def convert_age(value: str) -> float: """Convert age to float""" return None # Not used since age not available def convert_gender(value: str) -> int: """Convert gender to binary""" return None # Not used since gender not available # 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 # Since trait_row is not None, we need to extract clinical features clinical_df = 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 print("Preview of clinical features:") print(preview_df(clinical_df)) # Save clinical data clinical_df.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()) # The identifiers appear to be probeset IDs (ending in '_st') # rather than standard human gene symbols like 'BRCA1', 'TP53', etc. # These will need to be mapped to official 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) # Preview the first few rows print("\nPreview of the annotation data:") print(json.dumps(preview_df(gene_metadata), indent=2)) # Find probe IDs and gene symbols in annotation data # The gene expression data uses probeset_id format, which matches the 'ID' column in annotations # Gene symbols are in gene_assignment column with format "RefSeq // Gene Symbol // Description" mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') # Convert the gene assignment strings to gene symbols def extract_gene(assignment): if pd.isna(assignment): return [] # Split by gene name separator '//' and look for entries that appear to be gene symbols genes = [] parts = assignment.split('//') for part in parts: genes.extend(extract_human_gene_symbols(part)) return genes # Get the gene mapping and apply it to convert probe expression to gene expression gene_data = apply_gene_mapping(genetic_data, mapping_data) # Print preview of the gene data print("Preview of mapped gene expression data:") print(preview_df(gene_data)) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, 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 from gene expression microarray profiling of proximal tubule cells from African American individuals, comparing samples with different APOL1 genotypes." 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)