# Path Configuration from tools.preprocess import * # Processing context trait = "Lung_Cancer" cohort = "GSE244117" # Input paths in_trait_dir = "../DATA/GEO/Lung_Cancer" in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE244117" # Output paths out_data_file = "./output/preprocess/3/Lung_Cancer/GSE244117.csv" out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE244117.csv" out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE244117.csv" json_path = "./output/preprocess/3/Lung_Cancer/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data using specified prefixes background_info, clinical_data = get_background_and_clinical_data( matrix_file, prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'] ) # Get unique values per clinical feature sample_characteristics = get_unique_values_by_row(clinical_data) # Print background info print("Dataset Background Information:") print(f"{background_info}\n") # Print sample characteristics print("Sample Characteristics:") for feature, values in sample_characteristics.items(): print(f"Feature: {feature}") print(f"Values: {values}\n") # 1. Gene Expression Data Availability # Based on background info, this contains spatial transcriptomics data of human ONB samples is_gene_available = True # 2.1 Data Type Availability # Trait (lung cancer status) can be inferred from grade (1) trait_row = 1 # Age is available in row 5 age_row = 5 # Gender is available in row 4 gender_row = 4 # 2.2 Data Type Conversion Functions def convert_trait(x): if pd.isna(x): return None # Extract value after colon and strip whitespace val = x.split(':')[1].strip().lower() # Normal samples are controls (0), any grade is case (1) if val == 'normal': return 0 elif val in ['ii', 'iii', 'iv']: return 1 return None def convert_age(x): if pd.isna(x): return None try: # Extract numeric age value after colon return float(x.split(':')[1].strip()) except: return None def convert_gender(x): if pd.isna(x): return None # Extract value after colon and strip whitespace val = x.split(':')[1].strip().upper() # Convert F->0, M->1 if val == 'F': return 0 elif val == 'M': return 1 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: # Extract features using library function 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 extracted clinical features:") print(preview) # Save to CSV clinical_features.to_csv(out_clinical_data_file) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene expression data from matrix file gene_data = get_genetic_data(matrix_file) # Print first 20 row IDs and shape of data to help debug print("Shape of gene expression data:", gene_data.shape) print("\nFirst few rows of data:") print(gene_data.head()) print("\nFirst 20 gene/probe identifiers:") print(gene_data.index[:20]) # Inspect a snippet of raw file to verify identifier format import gzip with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: lines = [] for i, line in enumerate(f): if "!series_matrix_table_begin" in line: # Get the next 5 lines after the marker for _ in range(5): lines.append(next(f).strip()) break print("\nFirst few lines after matrix marker in raw file:") for line in lines: print(line) # Looking at the identifiers like A1BG, A1CF, A2M - these appear to be standard HGNC gene symbols # The gene names follow standard human gene nomenclature conventions and match known human genes # Therefore no mapping is needed requires_gene_mapping = False # Since gene symbols are already standardized, skip normalization gene_data.index = gene_data.index.astype(str) # Ensure string index gene_data.to_csv(out_gene_data_file) # Load clinical data from previous steps selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) # Handle missing values linked_data = handle_missing_values(linked_data, trait) # Evaluate bias in features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Record cohort information 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="Contains standardized gene expression data and clinical features." ) # Save data if usable if is_usable: linked_data.to_csv(out_data_file)