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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Melanoma"
cohort = "GSE189631"
# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE189631"
# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE189631.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE189631.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE189631.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
# Debug: Print paths and directory existence
print("Cohort directory path:", in_cohort_dir)
print("Directory exists:", os.path.exists(in_cohort_dir))
if os.path.exists(in_cohort_dir):
files = os.listdir(in_cohort_dir)
print("\nFiles in directory:", files)
# Look for gzipped files if regular files not found
matrix_files = [f for f in files if ('matrix' in f.lower() and f.endswith('.gz'))]
soft_files = [f for f in files if ('soft' in f.lower() and f.endswith('.gz'))]
if matrix_files and soft_files:
matrix_file = os.path.join(in_cohort_dir, matrix_files[0])
soft_file = os.path.join(in_cohort_dir, soft_files[0])
print("\nFound files:")
print("Matrix file:", matrix_file)
print("SOFT file:", soft_file)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("\nDataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
else:
print("\nRequired .gz files not found in directory")
else:
print("\nDirectory does not exist") |