output/preprocess/Cystic_Fibrosis/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"

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

# Output paths
out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/TCGA.csv"
out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/TCGA.csv"
json_path = "./output/z2/preprocess/Cystic_Fibrosis/cohort_info.json"


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

# List subdirectories under TCGA root
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 matching Cystic Fibrosis (CF). TCGA is cancer-focused; CF is not a cancer.
# Only match strict synonyms to avoid inappropriate selection.
keywords = ["cystic fibrosis", "mucoviscidosis", "cf"]
matched_dirs = []
for d in subdirs:
    name_l = d.lower()
    if any(k in name_l for k in keywords):
        matched_dirs.append(d)

if len(matched_dirs) == 0:
    # No suitable cohort found; record and skip this trait for TCGA
    _ = 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:
    # If multiple matches, choose the most specific (longest name as proxy)
    selected_dir = sorted(matched_dirs, key=len, reverse=True)[0]
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)

    # Locate clinical and genetic file paths
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # Load dataframes
    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')

    # Print clinical columns
    print(clinical_df.columns.tolist())