output/preprocess/Craniosynostosis/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Craniosynostosis"

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

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


# Step 1: Initial Data Loading
# Step 1: Identify the most relevant TCGA subdirectory for Craniosynostosis (none expected)
subdirs = os.listdir(tcga_root_dir)

# Define keywords related to Craniosynostosis
keywords = [
    ("craniosynostosis", 5),
    ("synostosis", 4),
    ("cranio", 3),
    ("skull", 2),
    ("suture", 1),
]

def score_dir(name: str) -> int:
    lname = name.lower()
    return max((w for k, w in keywords if k in lname), default=0)

scored = [(d, score_dir(d)) for d in subdirs]
# Select dir with highest score if any > 0
selected_dir = None
if scored:
    best_dir, best_score = max(scored, key=lambda x: x[1])
    if best_score > 0:
        selected_dir = best_dir

clinical_df, genetic_df = pd.DataFrame(), pd.DataFrame()

if selected_dir is None:
    # No suitable TCGA cohort for Craniosynostosis; 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. Clinical columns: []")
else:
    # Step 2: Identify file paths
    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 both files as DataFrames
    def read_tcga_file(path: str) -> pd.DataFrame:
        compression = 'gzip' if path.endswith('.gz') else None
        return pd.read_csv(path, sep='\t', index_col=0, low_memory=False, compression=compression)

    clinical_df = read_tcga_file(clinical_file_path)
    genetic_df = read_tcga_file(genetic_file_path)

    # Step 4: Print column names of the clinical data
    print(list(clinical_df.columns))