File size: 2,148 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 57 58 59 60 61 62 63 64 65 66 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Bipolar_disorder"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Bipolar_disorder/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Bipolar_disorder/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Initialize placeholders for downstream steps
selected_subdir = None
clinical_df = None
genetic_df = None
# Discover subdirectories
try:
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
except Exception as e:
subdirs = []
print(f"ERROR: Unable to list TCGA root directory '{tcga_root_dir}': {e}")
# Attempt to find a TCGA cohort relevant to Bipolar disorder (unlikely within TCGA cancer cohorts)
trait_keywords = {
"bipolar", "bipolar_disorder", "bipolar disorder", "mania", "manic"
}
candidates = []
for d in subdirs:
name_l = d.lower()
if any(k in name_l for k in trait_keywords):
candidates.append(d)
# Select the most specific match if any (longest name heuristic)
if candidates:
selected_subdir = sorted(candidates, key=len, reverse=True)[0]
if selected_subdir is None:
print(f"No suitable TCGA cohort found for trait '{trait}'. Skipping this trait.")
# Record that this trait is not applicable for TCGA
validate_and_save_cohort_info(
is_final=False,
cohort="TCGA",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
else:
cohort_dir = os.path.join(tcga_root_dir, selected_subdir)
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
# Load 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)
# Print clinical column names for inspection
print(list(clinical_df.columns)) |