p3/preprocess/Liver_Cancer/code/GSE174570.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE174570"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE174570"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE174570.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE174570.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE174570.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"

# Get file paths for soft and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability 
# Yes - using Affymetrix Human Genome U219 Array
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Disease state (trait) is in row 0, has two values (HCC vs control)
trait_row = 0

# Age and gender not available in characteristics 
age_row = None
gender_row = None

# Convert disease state to binary (HCC = 1, Non-tumour/control = 0)
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if 'hcc' in value:
        return 1
    return 0

def convert_age(value):
    return None

def convert_gender(value): 
    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. Extract clinical features
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(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)
    
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical))
    
    selected_clinical.to_csv(out_clinical_data_file)