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fcf7aea | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Angelman_Syndrome"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Angelman_Syndrome/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Angelman_Syndrome/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Angelman_Syndrome/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Angelman_Syndrome/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Step 1: Identify the most relevant TCGA cohort directory for Angelman Syndrome (none expected)
available_dirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
search_terms = {"angelman", "ube3a"}
matching_dirs = [d for d in available_dirs if any(term in d.lower() for term in search_terms)]
selected_dir = None
if matching_dirs:
# If multiple, choose the most specific (heuristic: longest name)
selected_dir = sorted(matching_dirs, key=len, reverse=True)[0]
if selected_dir is None:
# No suitable TCGA cancer cohort matches Angelman Syndrome; record and stop
_ = validate_and_save_cohort_info(
is_final=False,
cohort="TCGA",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
else:
cohort_dir = os.path.join(tcga_root_dir, selected_dir)
# Step 2: Identify clinical and genetic file paths
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')
genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
# Step 4: Print clinical column names
print(clinical_df.columns.tolist()) |