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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "HIV_Resistance"
cohort = "GSE117748"
# Input paths
in_trait_dir = "../DATA/GEO/HIV_Resistance"
in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE117748"
# Output paths
out_data_file = "./output/preprocess/3/HIV_Resistance/GSE117748.csv"
out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE117748.csv"
out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE117748.csv"
json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data Availability
# This is a miRNA study on cell lines (based on the title and sample characteristics)
is_gene_available = False
# 2.1 Data Availability
# From sample characteristics, no human trait, age or gender data available
trait_row = None
age_row = None
gender_row = None
# 2.2 Data Type Conversion functions (not used but defined for completeness)
def convert_trait(x):
return None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save Metadata
# Validate and save cohort info - initial filtering
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False # trait_row is None
)
# 4. Clinical Feature Extraction
# Skip since trait_row is None |