output/preprocess/Allergies/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"

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

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Allergies/cohort_info.json"


# Step 1: Initial Data Loading
import os
import pandas as pd

# Step 1: Identify the most appropriate TCGA subdirectory for the trait "Allergies"
# Since TCGA cohorts are cancer types and none relate to allergies, we attempt a keyword search.
keywords = [
    "allerg", "hypersens", "atopy", "atopic", "asthma", "urticaria", "rhinitis", "eczema", "hayfever", "hay_fever"
]

# List available TCGA subdirectories
available_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Find candidates whose names contain any allergy-related keyword
candidates = [d for d in available_subdirs if any(k in d.lower() for k in keywords)]

selected_dir = None
if len(candidates) > 0:
    # If multiple matches, choose the one with the longest keyword overlap (more specific)
    def score_dir(name: str) -> int:
        lname = name.lower()
        return sum(lname.count(k) for k in keywords)

    candidates.sort(key=score_dir, reverse=True)
    selected_dir = candidates[0]

# If no suitable directory found, mark as skipped and stop here
if selected_dir is None:
    print("No TCGA cohort directory matches the target trait 'Allergies'. Skipping this trait.")
    # Record as unavailable for this trait
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    clinical_df = None
    genetic_df = None
else:
    print(f"Selected TCGA cohort directory: {selected_dir}")
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)

    # Step 2: Identify clinical and genetic file paths
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
    print(f"Clinical file: {clinical_file_path}")
    print(f"Genetic file:  {genetic_file_path}")

    # Step 3: Load both files
    clinical_df = pd.read_csv(clinical_file_path, sep="\t", index_col=0, low_memory=False)
    genetic_df = pd.read_csv(genetic_file_path, sep="\t", index_col=0, low_memory=False)

    # Step 4: Print column names of the clinical data
    print("Clinical data columns:")
    print(list(clinical_df.columns))