# Path Configuration from tools.preprocess import * # Processing context trait = "Thymoma" cohort = "GSE29695" # Input paths in_trait_dir = "../DATA/GEO/Thymoma" in_cohort_dir = "../DATA/GEO/Thymoma/GSE29695" # Output paths out_data_file = "./output/preprocess/3/Thymoma/GSE29695.csv" out_gene_data_file = "./output/preprocess/3/Thymoma/gene_data/GSE29695.csv" out_clinical_data_file = "./output/preprocess/3/Thymoma/clinical_data/GSE29695.csv" json_path = "./output/preprocess/3/Thymoma/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 shape and first few rows to verify data print("Background Information:") print(background_info) print("\nClinical Data Shape:", clinical_data.shape) print("\nFirst few rows of Clinical Data:") print(clinical_data.head()) 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 the background information, this is a gene expression dataset using Illumina BeadStudio platform is_gene_available = True # 2.1 Data Availability # Trait can be determined from type/category field (row 1 or 2) trait_row = 1 # Using the type field which has more granular classification # Age and gender not available age_row = None gender_row = None # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: """Convert thymoma subtype to binary (0=less aggressive, 1=more aggressive)""" if not value or ':' not in value: return None type_val = value.split(':')[1].strip() # B3 is most aggressive subtype if 'B3' in type_val: return 1 # B2, B1/B2 are intermediate aggression elif 'B2' in type_val or 'B1/B2' in type_val: return 1 # A, AB, B1 are less aggressive elif type_val in ['A', 'AB', 'B1', 'Mixed AB', 'A/B']: return 0 # Cell lines should be excluded elif type_val == 'CL': return None return None def convert_age(value: str) -> float: return None # Age not available def convert_gender(value: str) -> int: return None # Gender not available # 3. Save initial 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 since trait_row is not None 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) print("\nPreview of processed clinical data:") print(preview_df(clinical_df)) # Save clinical data clinical_df.to_csv(out_clinical_data_file) # Extract gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Print first 20 row IDs print("First 20 gene/probe IDs:") print(list(genetic_data.index[:20])) # The gene identifiers have the prefix "ILMN_" which indicates they are Illumina probe IDs # These are not standard human gene symbols and will need to be mapped requires_gene_mapping = True # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # Preview annotation structure preview = preview_df(gene_annotation) print("Gene annotation preview:") print(preview) # 1. Identify relevant columns for mapping # 'ID' column in annotation matches the ILMN_* probe IDs in expression data # 'Symbol' column contains the human gene symbols probe_col = 'ID' gene_col = 'Symbol' # 2. Get mapping dataframe mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col) # 3. Apply mapping to convert probe data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Preview first few rows of converted data print("\nPreview of gene expression data after mapping:") print(preview_df(gene_data)) # 1. Normalize gene symbols in gene expression data gene_data = normalize_gene_symbols_in_index(gene_data) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) print("\nGene data shape (normalized gene-level):", gene_data.shape) # Load clinical data previously processed selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) print("\nClinical data shape:", selected_clinical_df.shape) # 2. Link clinical and genetic data using normalized gene-level data linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) print("\nLinked data shape:", linked_data.shape) # 3. Handle missing values systematically if trait in linked_data.columns: linked_data = handle_missing_values(linked_data, trait) # 4. Check for bias in trait and demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Final validation and information saving note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database." 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 and not biased if is_usable and not trait_biased: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)