output/preprocess/Alcohol_Flush_Reaction/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Alcohol_Flush_Reaction"

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

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


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

# Step 1: Identify the best-matching TCGA cohort directory for the trait "Alcohol_Flush_Reaction"
keywords = {
    'alcohol', 'ethanol', 'flush', 'flushing', 'reaction', 'erythema',
    'aldehyde', 'dehydrogenase', 'aldh2', 'acetaldehyde', 'intolerance', 'sensitivity'
}

subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

def normalize_name(name: str) -> str:
    return name.replace('_', ' ').replace('(', ' ').replace(')', ' ').lower()

matches = []
for d in subdirs:
    norm = normalize_name(d)
    hit_count = sum(1 for k in keywords if k in norm)
    if hit_count > 0:
        matches.append((d, hit_count, len(norm)))

# If no suitable directory is found, mark as completed (skip this trait)
if not matches:
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    selected_dir = None
    clinical_df = None
    genetic_df = None
else:
    # Choose the most specific match: highest hit_count, then shortest name
    matches.sort(key=lambda x: (-x[1], x[2]))
    selected_dir = matches[0][0]

    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 the clinical and genetic data
    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))