output/preprocess/Alopecia/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Alopecia"

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

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


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

# Step 1: Identify the most relevant TCGA cohort directory for the trait "Alopecia"
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
trait_terms = ['alopecia', 'hair', 'hairloss', 'hair_loss', 'hypotrich', 'atrich', 'trichotillomania']
selected_subdir = None
for d in subdirs:
    name_l = d.lower()
    if any(term in name_l for term in trait_terms):
        selected_subdir = d
        break

clinical_df = None
genetic_df = None

if selected_subdir is None:
    # No suitable cohort found for Alopecia in TCGA; record and skip further processing.
    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.")
else:
    # Step 2: Locate clinicalMatrix and PANCAN files within the selected cohort directory
    cohort_dir = os.path.join(tcga_root_dir, selected_subdir)
    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)

    # Step 3: Load both files into DataFrames
    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)

    # Step 4: Print clinical column names
    print(clinical_df.columns.tolist())