# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_cirrhosis" cohort = "GSE139602" # Input paths in_trait_dir = "../DATA/GEO/Liver_cirrhosis" in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE139602" # Output paths out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE139602.csv" out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE139602.csv" out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE139602.csv" json_path = "./output/preprocess/3/Liver_cirrhosis/cohort_info.json" # Step 1: Get file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Step 2: Extract background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Step 3: Get dictionary of unique values for each clinical feature unique_values_dict = get_unique_values_by_row(clinical_data) # Step 4: Print background info and sample characteristics print("Dataset Background Information:") print("-" * 80) print(background_info) print("\nSample Characteristics:") print("-" * 80) print(json.dumps(unique_values_dict, indent=2)) # 1. Gene expression data availability # Yes, this dataset contains gene expression data based on the background description # which mentions "Transcriptome analysis on liver biopsies" is_gene_available = True # 2. Variable availability and conversion functions # 2.1 Identify rows containing data trait_row = 0 # Disease state is in row 0 age_row = None # Age data not available gender_row = None # Gender data not available # 2.2 Data type conversion functions def convert_trait(value): """Convert disease state to binary: 1 for cirrhosis (compensated/decompensated), 0 for others""" if not isinstance(value, str): return None value = value.split(": ")[-1].lower() if "cirrhosis" in value: return 1 elif value in ["healthy", "ecld", "acute-on-chronic liver failure"]: return 0 return None convert_age = None # Not needed since age data unavailable convert_gender = None # Not needed since gender data unavailable # 3. Save metadata - initial filtering 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. Extract clinical features if trait_row is not None: clinical_features = geo_select_clinical_features( clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait ) # Preview the extracted features preview = preview_df(clinical_features) print("Preview of clinical features:") print(preview) # Save clinical features os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) clinical_features.to_csv(out_clinical_data_file) # 1. Extract gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # 2. Print first 20 row IDs print("First 20 gene/probe identifiers:") print(genetic_data.index[:20]) # These look like Affymetrix probe IDs (format: ######_at or ######_s_at) # They need to be mapped to human gene symbols requires_gene_mapping = True # 1. Extract gene annotation data from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # 2. Preview annotation data print("Column names and first few values in gene annotation data:") print(preview_df(gene_annotation)) # 1. Identify columns for mapping: # 'ID' column in annotation matches probe IDs in gene expression data # 'Gene Symbol' column contains the corresponding gene symbols probe_col = 'ID' gene_col = 'Gene Symbol' # 2. Get gene mapping dataframe mapping_data = get_gene_mapping(gene_annotation, probe_col, gene_col) # 3. Convert probe measurements to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # 1. Normalize gene symbols and save gene data normalized_gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait="trait") # 4. Check for biased features and remove biased demographic ones is_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=True, is_trait_available=True, is_biased=is_biased, df=linked_data, note="All subjects are male according to series summary. Age information not available." ) # 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) # 1. Normalize gene symbols and save gene data normalized_gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait_col="trait") # 4. Check for biased features and remove biased demographic ones is_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=True, is_trait_available=True, is_biased=is_biased, df=linked_data, note="All subjects are male according to series summary. Age information not available." ) # 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) # 1. Normalize gene symbols and save gene data normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) normalized_gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features and remove biased demographic ones is_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=True, is_trait_available=True, is_biased=is_biased, df=linked_data, note="All subjects are male according to series summary. Age information not available." ) # 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)