File size: 2,401 Bytes
933cd71 | 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 67 68 69 70 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Arrhythmia"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/z1/preprocess/Arrhythmia/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
# Step 1: Initial Data Loading
import os
import pandas as pd
# Discover available TCGA subdirectories
available_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
# Attempt to find a cohort relevant to Arrhythmia (cardiac rhythm disorders)
keywords_specific = [
'arrhythmia', 'atrial_fibrillation', 'brugada', 'long_qt', 'ventricular_tachycardia',
'supraventricular', 'cardiac_conduction', 'torsades', 'wolff', 'wolff-parkinson-white'
]
keywords_general = ['cardiac', 'cardio', 'heart', 'myocard']
def find_best_cohort(subdirs, specific_kw, general_kw):
scored = []
for sd in subdirs:
sdl = sd.lower()
score = 0
if any(k in sdl for k in specific_kw):
score += 2
if any(k in sdl for k in general_kw):
score += 1
if score > 0:
scored.append((score, sd))
if not scored:
return None
scored.sort(reverse=True) # highest score first
return scored[0][1]
selected_subdir = find_best_cohort(available_subdirs, keywords_specific, keywords_general)
if selected_subdir is None:
print(f"No suitable TCGA cohort directory found for trait '{trait}'. Skipping this trait.")
# Record metadata for skipping
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 files
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(f"Selected cohort directory: {selected_subdir}")
print("Clinical data columns:")
print(list(clinical_df.columns)) |