# Path Configuration from tools.preprocess import * # Processing context trait = "Liver_Cancer" cohort = "GSE228783" # Input paths in_trait_dir = "../DATA/GEO/Liver_Cancer" in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE228783" # Output paths out_data_file = "./output/preprocess/3/Liver_Cancer/GSE228783.csv" out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE228783.csv" out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE228783.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 # Yes, this appears to be a gene expression dataset studying liver tissue, # not purely miRNA or methylation is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability # trait: Can be inferred from 'disease' field showing cancer types trait_row = 2 # Age and gender not available in characteristics age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value): if not value or 'disease:' not in value: return None value = value.split('disease:')[1].strip().lower() # Convert cancer types to binary - 1 for liver cancer (HCC), 0 for others if 'hcc' in value: return 1 return 0 def convert_age(value): # Not used since age data not available return None def convert_gender(value): # Not used since gender data not available return None # 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. Clinical Feature Extraction # Since trait_row is not None, extract clinical features clinical_features = 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 extracted features preview = preview_df(clinical_features) print("Preview of clinical features:") print(preview) # Save clinical data clinical_features.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()) # Observe the format of gene identifiers: they are probe IDs from an older version of the Affymetrix array platform # These probe IDs (e.g. "11715100_at") need to be mapped to human gene symbols for downstream analysis requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file) # Identify mapping columns prob_col = 'ID' # Column containing probe IDs gene_col = 'Gene Symbol' # Column containing gene symbols # Get mapping between probe IDs and gene symbols mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) # Preview the mapping data structure print("Gene Mapping Preview:") preview = preview_df(mapping_df) print(json.dumps(preview, indent=2)) # Apply gene mapping to convert probe expression into gene expression gene_data = apply_gene_mapping(genetic_data, mapping_df) # Preview gene expression data print("\nGene Expression Data Preview:") preview = preview_df(gene_data) print(json.dumps(preview, indent=2)) # Save gene data gene_data.to_csv(out_gene_data_file) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) # Debug print to check data before handling missing values print("\nPreview of linked data before handling missing values:") print(linked_data.head()) # 3. Handle missing values linked_data = handle_missing_values(df=linked_data, trait_col=trait) print("\nPreview of linked data after handling missing values:") print(linked_data.head()) # 4. Check for biases and remove biased demographic features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Validate dataset quality and save metadata note = "" if is_biased: note = "The trait distribution is severely biased." 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 linked data if usable if is_usable: linked_data.to_csv(out_data_file)