p3/preprocess/Post-Traumatic_Stress_Disorder/code/GSE52875.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Post-Traumatic_Stress_Disorder"
cohort = "GSE52875"

# Input paths
in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE52875"

# Output paths
out_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/GSE52875.csv"
out_gene_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/gene_data/GSE52875.csv"
out_clinical_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/clinical_data/GSE52875.csv"
json_path = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
is_gene_available = False  # Not suitable - mice dataset

# 2. Variable Availability and Data Type Conversion
# All data rows containing only strain and tissue info, no trait/age/gender data
trait_row = None
age_row = None 
gender_row = None

# These conversion functions won't be used since data not available
# but defining them to avoid NameErrors
def convert_trait(x):
    return None
    
def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save Metadata
# Initial filtering - dataset not suitable since it's mice data
validate_and_save_cohort_info(is_final=False,
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=False)

# 4. Skip clinical feature extraction since trait_row is None