# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_Cancer" cohort = "GSE66843" # Input paths in_trait_dir = "../DATA/GEO/Liver_Cancer" in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE66843" # Output paths out_data_file = "./output/preprocess/3/Liver_Cancer/GSE66843.csv" out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE66843.csv" out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE66843.csv" json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json" # Get file paths for soft and matrix files soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Get background info and clinical data from matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file) # Get unique values for each clinical feature row clinical_features = get_unique_values_by_row(clinical_data) # Print background info print("Background Information:") print(background_info) print("\nClinical Features and Sample Values:") print(json.dumps(clinical_features, indent=2)) # 1. Gene Expression Data Availability # This appears to be an in-vitro cell line study with Huh7.5.1 cells infected with HCV # It's likely to contain gene expression data measuring transcriptional changes is_gene_available = True # 2.1 Data Availability # trait: binary infected vs control status can be determined from infection status in row 1 trait_row = 1 # age: not applicable for cell line data age_row = None # gender: not applicable for cell line data gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): # Extract value after colon if ':' in str(value): value = value.split(':')[1].strip() # Mock infection (control) = 0, HCV infection = 1 if 'Mock' in value or 'control' in value: return 0 elif 'HCV' in value: return 1 return None def convert_age(value): return None # Not used def convert_gender(value): return None # Not used # 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 if trait_row is not None: selected_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 the data print("Preview of selected clinical features:") print(preview_df(selected_clinical_df)) # Save to CSV selected_clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_data = get_genetic_data(matrix_file) # Print DataFrame info and dimensions to verify data structure print("DataFrame info:") print(genetic_data.info()) print("\nDataFrame dimensions:", genetic_data.shape) # Print an excerpt of the data to inspect row/column structure print("\nFirst few rows and columns of data:") print(genetic_data.head().iloc[:, :5]) # Print first 20 row IDs print("\nFirst 20 gene/probe IDs:") print(genetic_data.index[:20].tolist()) # The index shows ILMN_ prefixes which indicates these are Illumina probe IDs # These need to be mapped to standard human gene symbols for analysis requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file) # Preview the annotation data structure print("Gene Annotation Preview:") preview = preview_df(gene_annotation) print(json.dumps(preview, indent=2)) print("\nGene Annotation Analysis:") print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.") print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.") # Update validation info to show dataset cannot be used due to missing gene mapping validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=False, # Set to False since gene expression data is not mappable is_trait_available=trait_row is not None, note="Dataset contains numeric probe IDs but lacks gene symbol mapping information" )