output/preprocess/Asthma/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"

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

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


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

# Step 1: Identify a TCGA cohort relevant to the trait "Asthma"
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Define strict synonyms for asthma; avoid overly broad respiratory terms to prevent mismatches (e.g., lung cancers)
asthma_terms = {"asthma", "bronchial_asthma", "asthmatic"}
candidates = []
for d in subdirs:
    name_lower = d.lower()
    if any(term in name_lower for term in asthma_terms):
        candidates.append(d)

selected_dir = None
if candidates:
    # Choose the most specific (shortest name as a heuristic)
    selected_dir = sorted(candidates, key=lambda x: len(x))[0]

if selected_dir is None:
    print("No suitable TCGA cohort found for Asthma. Skipping this trait.")
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
else:
    # Step 2: Locate clinical and genetic files
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # 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 clinical column names
    print(list(clinical_df.columns))