File size: 2,229 Bytes
e9da0d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # 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)) |