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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Bone_Density"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/1/Bone_Density/TCGA.csv"
out_gene_data_file = "./output/preprocess/1/Bone_Density/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/1/Bone_Density/clinical_data/TCGA.csv"
json_path = "./output/preprocess/1/Bone_Density/cohort_info.json"
import os
import pandas as pd
# Step 1: Check directories in tcga_root_dir for anything relevant to "Bone_Density"
search_terms = ["bone", "density", "osteoporosis", "bone_density"]
dir_list = os.listdir(tcga_root_dir)
matching_dir = None
for d in dir_list:
d_lower = d.lower()
if any(term in d_lower for term in search_terms):
matching_dir = d
break
if matching_dir is None:
# No matching directory found. Mark the dataset as skipped.
validate_and_save_cohort_info(
is_final=False,
cohort="TCGA_Bone_Density",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
else:
# 2. Identify the clinicalMatrix and PANCAN files
cohort_dir = os.path.join(tcga_root_dir, matching_dir)
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# 3. Load both data files
clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
# 4. Print the column names of the clinical data
print("Clinical Data Columns:")
print(clinical_df.columns.tolist()) |