# Path Configuration from tools.preprocess import * # Processing context trait = "Melanoma" cohort = "GSE200904" # Input paths in_trait_dir = "../DATA/GEO/Melanoma" in_cohort_dir = "../DATA/GEO/Melanoma/GSE200904" # Output paths out_data_file = "./output/preprocess/3/Melanoma/GSE200904.csv" out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE200904.csv" out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE200904.csv" json_path = "./output/preprocess/3/Melanoma/cohort_info.json" # Get file paths for SOFT and matrix files soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data from the matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Create dictionary of unique values for each feature unique_values_dict = get_unique_values_by_row(clinical_data) # Print the information print("Dataset Background Information:") print(background_info) print("\nSample Characteristics:") for feature, values in unique_values_dict.items(): print(f"\n{feature}:") print(values) # 1. Gene Expression Data Availability # Based on series title "NanoString nCounter" and overall design mentioning "Multiplex gene expression analysis" is_gene_available = True # 2.1 Data Availability # All samples are melanoma cases, can use scan_id row trait_row = 0 # No age or gender data available age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(x): # All samples are melanoma cases from tissue microarrays return 1 def convert_age(x): return None def convert_gender(x): 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_df = 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_df(clinical_df) clinical_df.to_csv(out_clinical_data_file) # Extract genetic data matrix genetic_data = get_genetic_data(matrix_file_path) # Print first 20 row IDs to examine data type print("First 20 row IDs:") print(list(genetic_data.index)[:20]) # After examining the IDs and confirming this is gene expression data: is_gene_available = True # Save updated 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) ) genetic_data.to_csv(out_gene_data_file) # These appear to be standard human gene symbols (e.g. AKT1, CD274/PD-L1, CD8A) commonly used in immunology/oncology # No mapping required as they are already in the correct format requires_gene_mapping = False # 1. Normalize gene symbols and save gene data gene_data = normalize_gene_symbols_in_index(genetic_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 clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Judge bias in features and remove biased ones trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and save metadata is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=True, is_biased=trait_biased, df=linked_data, note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome." ) # 6. Save linked data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)