# Path Configuration from tools.preprocess import * # Processing context trait = "Sarcoma" cohort = "GSE159848" # Input paths in_trait_dir = "../DATA/GEO/Sarcoma" in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159848" # Output paths out_data_file = "./output/preprocess/3/Sarcoma/GSE159848.csv" out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE159848.csv" out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE159848.csv" json_path = "./output/preprocess/3/Sarcoma/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("Background Information:") print(background_info) 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 # The dataset uses "Agilent-014850 Whole Human Genome Microarray", so it contains gene expression data is_gene_available = True # 2. Variable Availability and Row Identification trait_row = 3 # metastasis data is available in row 3 age_row = 1 # age data is available in row 1 gender_row = 0 # gender data is available in row 0 # Define conversion functions def convert_trait(value: str) -> Optional[float]: """Convert metastasis status to binary: 0 = no metastasis, 1 = has metastasis""" if not value or 'metastasis:' not in value: return None try: return float(value.split(': ')[1]) except: return None def convert_age(value: str) -> Optional[float]: """Convert age to continuous values""" if not value or 'age:' not in value: return None try: return float(value.split(': ')[1]) except: return None def convert_gender(value: str) -> Optional[float]: """Convert gender to binary: 0 = female, 1 = male""" if not value or 'Sex:' not in value: return None gender = value.split(': ')[1].strip().upper() if gender == 'F': return 0.0 elif gender == 'M': return 1.0 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. Extract clinical features 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_df(selected_clinical_df)) # Save clinical data selected_clinical_df.to_csv(out_clinical_data_file) # Get gene expression data from matrix file genetic_data = get_genetic_data(matrix_file_path) # Examine data structure print("Data structure and head:") print(genetic_data.head()) print("\nShape:", genetic_data.shape) print("\nFirst 20 row IDs (gene/probe identifiers):") print(list(genetic_data.index)[:20]) # Get a few column names to verify sample IDs print("\nFirst 5 column names:") print(list(genetic_data.columns)[:5]) # The identifiers start with 'A_23_P', which appear to be Agilent probe IDs rather than standard human gene symbols # These need to be mapped to their corresponding gene symbols for analysis requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Preview column names and values from annotation dataframe print("Gene annotation DataFrame preview:") print(preview_df(gene_annotation)) # 1. From the preview, we can see that 'ID' contains probe identifiers matching gene expression data, # and 'GENE_SYMBOL' contains the gene symbols probe_col = 'ID' gene_col = 'GENE_SYMBOL' # 2. Get mapping between probe IDs and gene symbols gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col) # 3. Convert probe-level expression to gene-level expression gene_data = apply_gene_mapping(genetic_data, gene_mapping) # Save gene expression data to file gene_data.to_csv(out_gene_data_file) # Preview result print("\nGene expression data preview:") print(preview_df(gene_data)) print("\nShape:", gene_data.shape) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) print("Gene data shape after normalization:", gene_data.shape) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) # 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 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 = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait." 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 if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file) else: # Handle case where clinical features were not properly extracted note = "Failed to extract clinical trait information from sample characteristics." validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=None, df=None, note=note )