File size: 2,018 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 58 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Depression"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z2/preprocess/Depression/TCGA.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/TCGA.csv"
json_path = "./output/z2/preprocess/Depression/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Step 1: Select TCGA subdirectory relevant to the trait "Depression"
keywords = [
'depress', 'mdd', 'major_depress', 'depressive', 'mood',
'psychi', 'mental', 'affective', 'sadness'
]
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
matches = []
for d in subdirs:
name_l = d.lower()
if any(k in name_l for k in keywords):
matches.append(d)
selected_tcga_dir = None
if len(matches) > 0:
# Choose the most specific match by the longest directory name (heuristic for specificity)
selected_tcga_dir = max(matches, key=len)
else:
# No suitable cohort for depression in TCGA cancer cohorts; record and skip this trait
print("No suitable TCGA cohort found for trait 'Depression'. Skipping preprocessing for this trait.")
_ = validate_and_save_cohort_info(
is_final=False,
cohort="TCGA",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
# Step 2-4: If a directory was selected, identify files, load data, and print clinical column names
clinical_df, genetic_df = None, None
if selected_tcga_dir is not None:
cohort_dir = os.path.join(tcga_root_dir, selected_tcga_dir)
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
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(list(clinical_df.columns)) |