# Path Configuration from tools.preprocess import * # Processing context trait = "Arrhythmia" cohort = "GSE55231" # Input paths in_trait_dir = "../DATA/GEO/Arrhythmia" in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE55231" # Output paths out_data_file = "./output/preprocess/3/Arrhythmia/GSE55231.csv" out_gene_data_file = "./output/preprocess/3/Arrhythmia/gene_data/GSE55231.csv" out_clinical_data_file = "./output/preprocess/3/Arrhythmia/clinical_data/GSE55231.csv" json_path = "./output/preprocess/3/Arrhythmia/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 # Based on the background info mentioning "gene expression" and "Illumina Human HT12 Version 4 BeadChips" # for transcription profiling with >47,000 transcripts, this dataset contains gene expression data is_gene_available = True # 2.1 Data Row Identification # For arrhythmia trait - not explicitly available in sample characteristics trait_row = None # Age is in row 2 (0-based index) age_row = 2 # Gender is in row 0 (0-based index) gender_row = 0 # 2.2 Data Type Conversion Functions def convert_trait(x): # Not needed as trait data is not available return None def convert_age(x): # Extract numeric age value after colon try: age = int(x.split(': ')[1]) return age # Return as continuous value except: return None def convert_gender(x): # Convert to binary: female=0, male=1 try: gender = x.split(': ')[1].lower() if gender == 'female': return 0 elif gender == 'male': return 1 return None except: return None # 3. Save Metadata is_trait_available = trait_row is not None 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, meaning clinical data for the trait is not available # 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 identifiers starting with "ILMN_" are Illumina probe IDs, not gene symbols # These need to be mapped to standard gene symbols for proper analysis 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 and gene expression data again soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) gene_data = get_genetic_data(matrix_file) # Map Illumina probe IDs to gene symbols mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol') # Convert probe-level measurements to gene expression values gene_data = apply_gene_mapping(gene_data, mapping_df) # Preview the mapped gene data print("Mapped Gene Expression Data Preview:") print("Shape:", gene_data.shape) print("\nFirst few rows:") print(gene_data.head()) # Since we determined trait data is not available, skip further processing note = "Dataset lacks trait information necessary for analysis" validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=False, is_biased=False, # Not applicable but required by function df=pd.DataFrame(), # Empty DataFrame but required by function note=note )