p3/preprocess/Endometrioid_Cancer/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"

# Input paths
tcga_root_dir = "../DATA/TCGA"

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

# First list available directories to verify
print("Available directories in TCGA root:")
tcga_dirs = os.listdir(tcga_root_dir)
print(tcga_dirs)

# Get Endometrioid Cancer cohort directory path (UCEC = Uterine Corpus Endometrial Carcinoma)
cohort_name = "TCGA.UCEC.sampleMap" # Directory containing endometrial cancer data
cohort_dir = os.path.join(tcga_root_dir, cohort_name)

# Get clinical and genetic file paths 
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load clinical and genetic data
clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)

# Print clinical data columns for analysis
print("\nClinical data columns:")
print(clinical_df.columns.tolist())

# Record data availability in metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA",
    info_path=json_path, 
    is_gene_available=len(genetic_df.columns) > 0,
    is_trait_available=True # Since we found the endometrial cancer directory
)
# First verify the root directory exists and print contents
print(f"TCGA root directory path: {tcga_root_dir}")
if os.path.exists(tcga_root_dir):
    print("Directory exists. Contents:", os.listdir(tcga_root_dir))
    
    # Get Endometrioid Cancer cohort directory path
    cohort_dir = os.path.join(tcga_root_dir, "TCGA_Endometrioid_Cancer_(UCEC)")

    # Get clinical and genetic file paths
    clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

    # Load clinical and genetic data
    clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
    genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)

    # Print clinical data columns for analysis
    print("\nClinical data columns:")
    print(clinical_df.columns.tolist())

    # Record data availability in metadata
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=len(genetic_df.columns) > 0,
        is_trait_available=True  # Since we're using the endometrial cancer directory
    )
else:
    print("Directory does not exist")
    # Record unavailability in metadata
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )