# Path Configuration from tools.preprocess import * # Processing context trait = "Hypertension" cohort = "GSE71994" # Input paths in_trait_dir = "../DATA/GEO/Hypertension" in_cohort_dir = "../DATA/GEO/Hypertension/GSE71994" # Output paths out_data_file = "./output/preprocess/3/Hypertension/GSE71994.csv" out_gene_data_file = "./output/preprocess/3/Hypertension/gene_data/GSE71994.csv" out_clinical_data_file = "./output/preprocess/3/Hypertension/clinical_data/GSE71994.csv" json_path = "./output/preprocess/3/Hypertension/cohort_info.json" # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data background_info, clinical_data = get_background_and_clinical_data(matrix_file) # 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 is_gene_available = True # Series summary mentions genome-wide gene expression analysis of PBMCs # 2.1 Data Availability # Trait (hypertension): Based on systolic blood pressure in samples a/b # We can identify controlled vs uncontrolled hypertensives using BP values trait_row = 6 # Using systolic BP from sample a # Age is available in Feature 3 age_row = 3 # Gender is available in Feature 1 gender_row = 1 # 2.2 Data Type Conversion Functions def convert_trait(value: str) -> int: # Extract systolic BP value if value is None: return None try: bp = int(value.split(': ')[1]) # Based on standard guidelines, systolic BP >= 140 indicates uncontrolled hypertension return 1 if bp >= 140 else 0 except: return None def convert_age(value: str) -> float: if value is None: return None try: return float(value.split(': ')[1]) except: return None def convert_gender(value: str) -> int: if value is None: return None gender = value.split(': ')[1].lower() if gender == 'female': return 0 elif gender == 'male': 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. Extract Clinical Features result = 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 and save results preview_df(result) result.to_csv(out_clinical_data_file) # 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) # The identifiers appear to be non-standard numerical IDs (e.g. 7896746) # rather than human gene symbols (which are typically alphanumeric like BRCA1) # These look like probe IDs from a microarray platform that need to be mapped to gene symbols requires_gene_mapping = True # Get file paths using library function soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene annotation from SOFT file and get meaningful data gene_annotation = get_gene_annotation(soft_file) # Preview gene annotation data print("Gene annotation shape:", gene_annotation.shape) print("\nGene annotation preview:") print(preview_df(gene_annotation)) print("\nNumber of non-null values in each column:") print(gene_annotation.count()) # Print example rows showing the mapping information columns print("\nSample mapping columns ('ID' and 'gene_assignment'):") print("\nFirst 2 rows:") print(gene_annotation[['ID', 'gene_assignment']].head(2).to_string()) # Explain the format print("\nNote: Gene symbols can be found in the 'gene_assignment' column in the format:") print(" // // // ") print("For example 'OR4F4 // olfactory receptor...'") print("Multiple genes may be separated by ///") # From gene_annotation data, 'ID' column matches gene identifiers in expression data # 'gene_assignment' contains gene symbols in format " // // // " # Create mapping dataframe with ID and gene_assignment columns mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment') # Apply gene mapping to convert probe data to gene data gene_data = apply_gene_mapping(gene_data, mapping_df) # Preview the mapped gene data print("Shape of gene data after mapping:", gene_data.shape) print("\nFirst few rows of mapped gene data:") print(gene_data.head()) # 1. Load clinical data and save normalized gene data selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0) gene_data.index = gene_data.index.str.replace('-mRNA', '') 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) # 2. Link clinical and genetic data linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Check for biased features and remove them if needed is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # 5. Validate and save cohort info 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="Study comparing transcriptional profiles between idiopathic non-cirrhotic portal hypertension patients, cirrhosis patients, and normal controls" ) # 6. Save linked data if usable if is_usable: os.makedirs(os.path.dirname(out_data_file), exist_ok=True) linked_data.to_csv(out_data_file)