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ad1ce63 | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Epilepsy"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z3/preprocess/Epilepsy/TCGA.csv"
out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/TCGA.csv"
json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"
# Step 1: Initial Data Loading
import os
# Step 1: Select the most relevant TCGA cohort directory for the trait "Epilepsy"
synonyms = ["epilepsy", "seizure", "seizures", "ictal", "epileptic"]
all_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
matched_dirs = [d for d in all_dirs if any(s in d.lower() for s in synonyms)]
selected_cohort_dirname = None
if matched_dirs:
# Prefer exact 'epilepsy' match if present; otherwise take the first matched
prioritized = sorted(matched_dirs, key=lambda d: (0 if "epilepsy" in d.lower() else 1, d.lower()))
selected_cohort_dirname = prioritized[0]
if selected_cohort_dirname is None:
print("No suitable TCGA cohort found for the trait 'Epilepsy'. Skipping this trait.")
# Record unusable dataset status
_ = 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
gene_df = None
else:
# Step 2: Identify clinical and genetic data file paths
cohort_dir = os.path.join(tcga_root_dir, selected_cohort_dirname)
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# Step 3: Load both files as DataFrames
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
gene_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
# Step 4: Print column names of the clinical data
print(list(clinical_df.columns)) |