File size: 2,201 Bytes
72233eb | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Alcohol_Flush_Reaction"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Alcohol_Flush_Reaction/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Step 1: Identify the best-matching TCGA cohort directory for the trait "Alcohol_Flush_Reaction"
keywords = {
'alcohol', 'ethanol', 'flush', 'flushing', 'reaction', 'erythema',
'aldehyde', 'dehydrogenase', 'aldh2', 'acetaldehyde', 'intolerance', 'sensitivity'
}
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
def normalize_name(name: str) -> str:
return name.replace('_', ' ').replace('(', ' ').replace(')', ' ').lower()
matches = []
for d in subdirs:
norm = normalize_name(d)
hit_count = sum(1 for k in keywords if k in norm)
if hit_count > 0:
matches.append((d, hit_count, len(norm)))
# If no suitable directory is found, mark as completed (skip this trait)
if not matches:
_ = validate_and_save_cohort_info(
is_final=False,
cohort="TCGA",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
selected_dir = None
clinical_df = None
genetic_df = None
else:
# Choose the most specific match: highest hit_count, then shortest name
matches.sort(key=lambda x: (-x[1], x[2]))
selected_dir = matches[0][0]
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 the clinical and genetic data
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
# Step 4: Print clinical column names
print(list(clinical_df.columns)) |