output/preprocess/Fibromyalgia/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Fibromyalgia"

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

# Output paths
out_data_file = "./output/z3/preprocess/Fibromyalgia/TCGA.csv"
out_gene_data_file = "./output/z3/preprocess/Fibromyalgia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z3/preprocess/Fibromyalgia/clinical_data/TCGA.csv"
json_path = "./output/z3/preprocess/Fibromyalgia/cohort_info.json"


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

# Step 1: Select the most relevant TCGA cohort directory for Fibromyalgia (likely none)
synonym_terms = [
    'fibromyalgia',
    'myalgia',
    'chronic pain',
    'central sensitization',
    'musculoskeletal pain'
]

# List available subdirectories
all_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Find matches
matches = []
for d in all_subdirs:
    name_l = d.lower()
    score = sum(term in name_l for term in synonym_terms)
    if score > 0:
        matches.append((score, d))

if not matches:
    # No suitable TCGA cohort for Fibromyalgia; 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
    )
else:
    # Choose the most specific match (highest score, then longest matched directory name)
    matches.sort(key=lambda x: (x[0], len(x[1])), reverse=True)
    selected_dir = matches[0][1]
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)

    # Step 2: Identify clinical and genetic file paths
    clinical_fp, genetic_fp = tcga_get_relevant_filepaths(cohort_dir)

    # Step 3: Load both files
    clinical_df = pd.read_csv(clinical_fp, sep='\t', index_col=0, low_memory=False)
    genetic_df = pd.read_csv(genetic_fp, sep='\t', index_col=0, low_memory=False)

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