p3/preprocess/Prostate_Cancer/code/GSE259218.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE259218"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE259218"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE259218.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE259218.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE259218.csv"
json_path = "./output/preprocess/3/Prostate_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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
is_gene_available = False  # This is just miRNA and hypoxia marker data according to background info

# 2. Variable Analysis
# All clinical data is unavailable as this is a cell line experiment, not patient data
trait_row = None  # No disease status as these are just cell lines
age_row = None  # Age not applicable for cell lines  
gender_row = None  # Gender not applicable for cell lines

# Define conversion functions despite not using them
def convert_trait(x):
    return None

def convert_age(x): 
    return None

def convert_gender(x):
    return None

# 3. Save Metadata 
# No trait data available since this is cell line data
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 this step as trait_row is None