File size: 1,969 Bytes
56598a1 | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Atrial_Fibrillation"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Atrial_Fibrillation/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Atrial_Fibrillation/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Atrial_Fibrillation/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Atrial_Fibrillation/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Discover available TCGA cohort directories
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
# Try to find a cohort relevant to atrial fibrillation (unlikely in TCGA cancer cohorts)
keywords = ['atrial_fibrillation', 'atrial fibrillation', 'a-fib', 'afib', 'arrhythmia', 'cardiac', 'heart']
candidates = []
for d in subdirs:
name_l = d.lower()
score = sum(1 for k in keywords if k in name_l)
if score > 0:
candidates.append((score, d))
selected_dir = None
if candidates:
# Choose the highest scoring (most specific) match
candidates.sort(key=lambda x: (-x[0], len(x[1])))
selected_dir = candidates[0][1]
if selected_dir is None:
# No suitable TCGA cohort for Atrial Fibrillation; 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 TCGA processing for this trait.")
else:
cohort_dir = os.path.join(tcga_root_dir, selected_dir)
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
print(clinical_df.columns.tolist()) |