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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Asthma"
cohort = "GSE184382"
# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE184382"
# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE184382.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE184382.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE184382.csv"
json_path = "./output/preprocess/3/Asthma/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# 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
# Based on series title and summary, this dataset contains miRNA data rather than gene expression data
is_gene_available = False
# 2. Variable Availability and Data Type Conversion
# 2.1 Check data availability from sample characteristics
trait_row = None # Cannot reliably determine asthma status from AIT treatment alone
age_row = None # Age data not available
gender_row = None # Gender data not available
# 2.2 Define conversion functions (though data not available in this case)
def convert_trait(x):
return None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save metadata about dataset usability
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=(trait_row is not None)
)
# 4. Clinical Feature Extraction
# Skip this step since trait_row is None |