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# 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) |