# Path Configuration from tools.preprocess import * # Processing context trait = "Stroke" cohort = "GSE47727" # Input paths in_trait_dir = "../DATA/GEO/Stroke" in_cohort_dir = "../DATA/GEO/Stroke/GSE47727" # Output paths out_data_file = "./output/preprocess/3/Stroke/GSE47727.csv" out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE47727.csv" out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE47727.csv" json_path = "./output/preprocess/3/Stroke/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 Availability # Yes, as series title mentions HumanHT-12 platform for gene expression profiling is_gene_available = True # 2. Variable Availability and Data Type Conversion # Age data is available in row 0 age_row = 0 def convert_age(value): if not value or ':' not in value: return None try: age = float(value.split(':')[1].strip()) return age except: return None # Gender data is available in row 1 gender_row = 1 def convert_gender(value): if not value or ':' not in value: return None gender = value.split(':')[1].strip().lower() if gender == 'female': return 0 elif gender == 'male': return 1 return None # Trait data not available since all participants are controls (constant) trait_row = None def convert_trait(value): # Not used since trait data unavailable return None # 3. Save metadata is_trait_available = False if trait_row is None else True 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. Clinical Feature Extraction # Skip since trait_row is None # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # 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 ILMN_ prefix indicates these are Illumina probe IDs # These need to be mapped to human gene symbols requires_gene_mapping = True # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file) # Preview annotation dataframe structure print("Gene Annotation Preview:") print("Column names:", gene_annotation.columns.tolist()) print("\nFirst few rows as dictionary:") print(preview_df(gene_annotation)) # Get file paths soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) # Load gene expression data gene_data = get_genetic_data(matrix_file) # Extract gene mapping using ID and Symbol columns since ID matches ILMN identifiers in expression data gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # Apply gene mapping to convert probe expressions to gene expressions gene_data = apply_gene_mapping(gene_data, gene_mapping) # Normalize gene symbols in index to standardized symbols and aggregate rows if needed gene_data = normalize_gene_symbols_in_index(gene_data) # Save processed gene expression data gene_data.to_csv(out_gene_data_file) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # 2. Load clinical data and link with genetic data clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) # 3. Handle missing values linked_data = handle_missing_values(linked_data, trait) # 4. Evaluate bias 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 examining transcriptome profiles from peripheral blood of older adults, including some with stroke history." ) # 6. Save linked data if usable if is_usable: linked_data.to_csv(out_data_file)