# Path Configuration from tools.preprocess import * # Processing context trait = "Kidney_Chromophobe" cohort = "GSE26574" # Input paths in_trait_dir = "../DATA/GEO/Kidney_Chromophobe" in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE26574" # Output paths out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE26574.csv" out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE26574.csv" out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE26574.csv" json_path = "./output/preprocess/3/Kidney_Chromophobe/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 is_gene_available = True # Based on series title and design indicating expression profiling # 2.1 Data Availability trait_row = 0 # Disease state contains trait info age_row = None # Age data not available gender_row = None # Gender data not available # 2.2 Data Type Conversion Functions def convert_trait(x): # Extract value after colon and strip whitespace val = x.split(':')[1].strip().lower() # Binary coding: 1 for Chromophobe, 0 for normal/other types if 'chromophobe' in val: return 1 elif 'normal' in val or val in ['ccrcc', 'pap_type1', 'pap_type2', 'hlrcc']: return 0 return None # Age and gender conversion functions not needed since data unavailable # 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 data is available selected_clinical = geo_select_clinical_features(clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait) # Preview the selected clinical features print("Preview of selected clinical features:") print(preview_df(selected_clinical)) # Save clinical data os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) selected_clinical.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 row IDs appear to be numerical indices rather than gene symbols # This indicates we need to map these identifiers to actual 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)) # 1. Observe that 'ID' in annotation matches the numeric IDs in gene expression data # and 'ORF' contains gene symbols # 2. Extract mapping between probe IDs and gene symbols mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF') # 3. Apply gene mapping to convert probe data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Preview the first few rows of mapped gene data print("Preview of gene expression data after mapping:") print(json.dumps(preview_df(gene_data), indent=2)) # 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(selected_clinical, 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 a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues." 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)