File size: 1,997 Bytes
6b8ee1b | 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 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Cystic_Fibrosis"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/TCGA.csv"
out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/TCGA.csv"
json_path = "./output/z2/preprocess/Cystic_Fibrosis/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# List subdirectories under TCGA root
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 matching Cystic Fibrosis (CF). TCGA is cancer-focused; CF is not a cancer.
# Only match strict synonyms to avoid inappropriate selection.
keywords = ["cystic fibrosis", "mucoviscidosis", "cf"]
matched_dirs = []
for d in subdirs:
name_l = d.lower()
if any(k in name_l for k in keywords):
matched_dirs.append(d)
if len(matched_dirs) == 0:
# No suitable cohort found; record and skip this trait for TCGA
_ = 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
genetic_df = None
else:
# If multiple matches, choose the most specific (longest name as proxy)
selected_dir = sorted(matched_dirs, key=len, reverse=True)[0]
cohort_dir = os.path.join(tcga_root_dir, selected_dir)
# Locate clinical and genetic file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# Load 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')
# Print clinical columns
print(clinical_df.columns.tolist()) |