# Path Configuration from tools.preprocess import * # Processing context trait = "Sarcoma" cohort = "GSE162785" # Input paths in_trait_dir = "../DATA/GEO/Sarcoma" in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162785" # Output paths out_data_file = "./output/preprocess/3/Sarcoma/GSE162785.csv" out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE162785.csv" out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE162785.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 is likely available since this is a microarray analysis is_gene_available = True # 2. Variable availability and data type conversion trait_row = 0 # cell line field contains information about Ewing Sarcoma cell lines age_row = None # Age data not available gender_row = None # Gender data not available def convert_trait(x): # Ewing Sarcoma (ES) cell lines indicate positive trait status # Extract cell line name after colon if not x or ':' not in x: return None cell_line = x.split(': ')[1].strip().upper() if cell_line in ['A673', 'CHLA-10', 'EW7', 'SK-N-MC']: return 1 # ES cell line return 0 # Not ES cell line def convert_age(x): return None # Not used since age data unavailable def convert_gender(x): return None # Not used since gender data unavailable # 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 since trait_row is not None clinical_features = geo_select_clinical_features(clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait) # Preview the extracted features print(preview_df(clinical_features)) # Save clinical data clinical_features.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 row IDs (7892501, 7892502, etc) appear to be probe IDs from a microarray platform # rather than human gene symbols like BRCA1, TP53, etc. # These numeric IDs need to be mapped to their corresponding gene symbols requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Preview annotation data print("Column names and first few values:") print(preview_df(gene_annotation)) # Based on the presence of "Homo sapiens" in the annotations, this is human data # The gene_assignment column appears to contain probe-to-gene mappings we need print("\nVerified human gene expression data with probe-to-gene mappings available.") # The 'ID' column in gene annotations contains probe IDs matching the gene expression data indices # The 'gene_assignment' column contains gene symbol information # Extract mapping relationship between probe IDs and gene symbols, extracting gene symbols from the complex annotations mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') # Apply the mapping to convert probe-level data to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_data) # Normalize/standardize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) # Print shape to verify the conversion print("\nGene expression data shape after mapping:", gene_data.shape) print("\nFirst few gene symbols:", list(gene_data.index)[:10]) # 1. Save normalized gene data (already normalized in previous step) os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) gene_data.to_csv(out_gene_data_file) print("Gene data shape:", 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 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 cohort consists entirely of Ewing sarcoma cell lines according to the background 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=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 )