output/preprocess/Atrial_Fibrillation/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"

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

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


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

# Discover available TCGA cohort directories
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Try to find a cohort relevant to atrial fibrillation (unlikely in TCGA cancer cohorts)
keywords = ['atrial_fibrillation', 'atrial fibrillation', 'a-fib', 'afib', 'arrhythmia', 'cardiac', 'heart']
candidates = []
for d in subdirs:
    name_l = d.lower()
    score = sum(1 for k in keywords if k in name_l)
    if score > 0:
        candidates.append((score, d))

selected_dir = None
if candidates:
    # Choose the highest scoring (most specific) match
    candidates.sort(key=lambda x: (-x[0], len(x[1])))
    selected_dir = candidates[0][1]

if selected_dir is None:
    # No suitable TCGA cohort for Atrial Fibrillation; record and skip
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    print("No suitable TCGA cohort found for the trait. Skipping TCGA processing for this trait.")
else:
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)

    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)

    print(clinical_df.columns.tolist())