# Path Configuration from tools.preprocess import * # Processing context trait = "Rectal_Cancer" cohort = "GSE145037" # Input paths in_trait_dir = "../DATA/GEO/Rectal_Cancer" in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE145037" # Output paths out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE145037.csv" out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE145037.csv" out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE145037.csv" json_path = "./output/preprocess/3/Rectal_Cancer/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) print("Background Information:") print(background_info) print("\nSample Characteristics:") # Get dictionary of unique values per row unique_values_dict = get_unique_values_by_row(clinical_data) for row, values in unique_values_dict.items(): print(f"\n{row}:") print(values) # 1. Gene Expression Data Availability # From background info, this dataset contains gene expression data from rectal cancer patients is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Row identifiers trait_row = 3 # response to CRT in row 3 age_row = 2 # age in row 2 gender_row = 1 # gender in row 1 # 2.2 Conversion functions def convert_trait(x): if not x: return None value = x.split(': ')[1].lower() return 0 if 'non-response' in value else 1 def convert_age(x): if not x: return None try: return int(x.split(': ')[1]) except: return None def convert_gender(x): if not x: return None value = x.split(': ')[1].lower() return 1 if 'male' in value else 0 # 3. Save metadata for initial filtering is_trait_avail = 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_avail) # 4. Extract clinical features selected_clinical = geo_select_clinical_features(clinical_data, trait="Response", 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 data preview_dict = preview_df(selected_clinical) print("Preview of extracted clinical data:") print(preview_dict) # Save clinical data selected_clinical.to_csv(out_clinical_data_file) # Get gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Examine data structure print("Data structure and head:") print(genetic_data.head()) print("\nShape:", genetic_data.shape) print("\nFirst 20 row IDs (gene/probe identifiers):") print(list(genetic_data.index)[:20]) # Get a few column names to verify sample IDs print("\nFirst 5 column names:") print(list(genetic_data.columns)[:5]) # The IDs are human gene symbols (e.g. A1BG, A1CF, A2M) and do not need mapping requires_gene_mapping = False # Reload clinical data that was processed earlier selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(genetic_data) genetic_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, "Response") # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "Response") # 5. Final validation and information saving note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response." 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=trait_biased, df=linked_data, note=note ) # 6. Save linked data only if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)