# Path Configuration from tools.preprocess import * # Processing context trait = "Mesothelioma" cohort = "GSE163720" # Input paths in_trait_dir = "../DATA/GEO/Mesothelioma" in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163720" # Output paths out_data_file = "./output/preprocess/3/Mesothelioma/GSE163720.csv" out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE163720.csv" out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE163720.csv" json_path = "./output/preprocess/3/Mesothelioma/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 # From background info, this is a microarray study of gene expression, particularly RERG is_gene_available = True # 2.1 Data Availability # Trait (mesothelioma) is constant for all samples so not available trait_row = None # Age is not available in sample characteristics age_row = None # Gender data available in row 2 gender_row = 2 # 2.2 Data Type Conversion Functions def convert_trait(x): return None # Not used since trait_row is None def convert_age(x): return None # Not used since age_row is None def convert_gender(x): if not isinstance(x, str): return None val = x.split(': ')[-1].strip().upper() 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=False # trait_row is None ) # 4. Clinical Feature Extraction 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 extracted clinical features preview = preview_df(clinical_df) print("Preview of clinical features:") print(preview) # Save clinical data 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 IDs start with "7892" which appear to be probe identifiers rather than gene symbols # Common in microarray data, these would need to be mapped to human gene symbols requires_gene_mapping = True # Extract gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Display column names and preview data print("Column names:") print(gene_annotation.columns) print("\nPreview of gene annotation data:") print(preview_df(gene_annotation)) # 1. Based on observation, the probe IDs in the expression data match 'ID' in the annotation, # and gene symbols are contained in 'gene_assignment' prob_col = 'ID' gene_col = 'gene_assignment' # 2. Get gene mapping dataframe from the annotation mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) # 3. Apply mapping to convert probe level data to gene level expression data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Preview results print("Mapping summary:") print("Number of probes in mapping:", len(mapping_df)) print("\nResulting gene expression data shape:", gene_data.shape) print("\nFirst 5 genes and their expression values:") print(gene_data.head()) # 1. Normalize gene symbols normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data) normalized_genetic_data.to_csv(out_gene_data_file) # Since clinical data is not usable (constant trait), create empty clinical data with Gender only clinical_df = pd.DataFrame({'Gender': [0 if x == 'Sex: F' else 1 for x in clinical_data.iloc[2]]}, index=clinical_data.columns[1:]) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_genetic_data) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, 'Gender') # 4. Check for bias in demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Gender') # 5. Final validation and information saving note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, # Constant trait (all tumor samples) is_biased=True, # Dataset is biased by design df=linked_data, note=note ) # 6. Skip saving linked data since dataset is not usable (is_usable will be False) if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file) # 1. Normalize gene symbols using gene expression data from previous step normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data) normalized_genetic_data.to_csv(out_gene_data_file) # Create clinical data with only Gender available clinical_df = pd.DataFrame({'Gender': [0 if x == 'Sex: F' else 1 for x in clinical_data.iloc[2]]}, index=clinical_data.columns[1:]) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_genetic_data) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, 'Gender') # 4. Check for bias in demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Gender') # 5. Final validation and information saving note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, # No trait data available is_biased=True, # Dataset is biased by design df=linked_data, note=note ) # 6. Skip saving linked data since dataset is not usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_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]) # Load gene annotation data gene_annotation = get_gene_annotation(soft_file_path) # Get gene expression data genetic_data = get_genetic_data(matrix_file_path) # 1. Based on observation, the probe IDs in the expression data match 'ID' in the annotation, # and gene symbols are contained in 'gene_assignment' prob_col = 'ID' gene_col = 'gene_assignment' # 2. Get gene mapping dataframe from the annotation mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) # 3. Apply mapping to convert probe level data to gene level expression data gene_data = apply_gene_mapping(genetic_data, mapping_df) # Preview results print("Mapping summary:") print("Number of probes in mapping:", len(mapping_df)) print("\nResulting gene expression data shape:", gene_data.shape) print("\nFirst 5 genes and their expression values:") print(gene_data.head()) # Convert probe-level to gene-level data gene_data = apply_gene_mapping(genetic_data, mapping_df) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(gene_data) genetic_data.to_csv(out_gene_data_file) # Create clinical data with Gender only clinical_df = pd.DataFrame({'Gender': [0 if x == 'Sex: F' else 1 for x in clinical_data.iloc[2]]}, index=clinical_data.columns[1:]) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data) # 3. Handle missing values systematically linked_data = handle_missing_values(linked_data, 'Gender') # 4. Check for bias in demographic features trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Gender') # 5. Final validation and information saving note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, # No trait data available is_biased=True, # Dataset is biased by design df=linked_data, note=note ) # 6. Skip saving linked data since dataset is not usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_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]) # 1. Normalize gene symbols genetic_data = normalize_gene_symbols_in_index(gene_data) genetic_data.to_csv(out_gene_data_file) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data) # 3. Handle missing values systematically 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 = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." 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=True, # Force biased=True since all samples are tumor samples df=linked_data, note=note ) # 6. Save linked data only if usable (which won't happen since is_biased=True) if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)