# Path Configuration from tools.preprocess import * # Processing context trait = "Asthma" cohort = "GSE230164" # Input paths in_trait_dir = "../DATA/GEO/Asthma" in_cohort_dir = "../DATA/GEO/Asthma/GSE230164" # Output paths out_data_file = "./output/preprocess/3/Asthma/GSE230164.csv" out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE230164.csv" out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE230164.csv" json_path = "./output/preprocess/3/Asthma/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 # Since "Gene expression profiling" is mentioned in series title, # and series summary indicates this is a SuperSeries containing SubSeries, # this dataset is likely to contain gene expression data is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Data Availability # Based on sample characteristics, we can find: # - Gender is available at key 0 # - Trait and age information are not explicitly available trait_row = None age_row = None gender_row = 0 # 2.2 Data Type Conversion Functions def convert_trait(x): return None # Not available def convert_age(x): return None # Not available def convert_gender(x): # Extract value after colon and convert to binary if not isinstance(x, str): return None value = x.split(': ')[-1].lower() if value == 'female': return 0 elif value == 'male': return 1 return None # 3. Save metadata about data availability 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. Since trait_row is None, skip clinical feature extraction # 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 start with ILMN_ which indicates they are Illumina probe IDs # These need to be mapped to human gene symbols for standardization 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)) # 1. Based on the preview, 'ID' in annotation matches probe IDs in expression data (ILMN_*), # and 'Symbol' contains gene symbols # 2. Extract ID and Symbol columns to create mapping dataframe gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') # 3. Apply gene mapping to convert probe-level measurements to gene-level expression gene_data = apply_gene_mapping(gene_data, gene_mapping) # 1. Normalize gene symbols gene_data = normalize_gene_symbols_in_index(gene_data) gene_data.to_csv(out_gene_data_file) # Create a minimal DataFrame for validation linked_data = gene_data.T # Transpose to have samples as rows # 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=False, is_biased=True, # No trait data means it's biased by definition df=linked_data, note="Dataset contains gene expression data but lacks required trait information." )