File size: 1,878 Bytes
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 52 53 54 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Alopecia"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Alopecia/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Alopecia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Alopecia/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Alopecia/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Step 1: Identify the most relevant TCGA cohort directory for the trait "Alopecia"
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
trait_terms = ['alopecia', 'hair', 'hairloss', 'hair_loss', 'hypotrich', 'atrich', 'trichotillomania']
selected_subdir = None
for d in subdirs:
name_l = d.lower()
if any(term in name_l for term in trait_terms):
selected_subdir = d
break
clinical_df = None
genetic_df = None
if selected_subdir is None:
# No suitable cohort found for Alopecia in TCGA; record and skip further processing.
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.")
else:
# Step 2: Locate clinicalMatrix and PANCAN files within the selected cohort directory
cohort_dir = os.path.join(tcga_root_dir, selected_subdir)
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
# Step 3: Load both files into DataFrames
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)
# Step 4: Print clinical column names
print(clinical_df.columns.tolist()) |