p3/preprocess/Von_Hippel_Lindau/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Von_Hippel_Lindau"

# Input paths
tcga_root_dir = "../DATA/TCGA"

# Output paths
out_data_file = "./output/preprocess/3/Von_Hippel_Lindau/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Von_Hippel_Lindau/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Von_Hippel_Lindau/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Von_Hippel_Lindau/cohort_info.json"

# Review all cohort directories
cohorts = os.listdir(tcga_root_dir)
cohorts = [c for c in cohorts if not c.startswith('.') and not c.endswith('.ipynb')]

# Select KIRC cohort since VHL syndrome is strongly associated with kidney clear cell carcinoma
selected_cohort = "TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)"
cohort_dir = os.path.join(tcga_root_dir, selected_cohort)

# Get paths for clinical and genetic data files
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load the 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')

# Print clinical data columns for inspection
print("Clinical data columns:")
print(clinical_df.columns.tolist())

# Validate data availability 
validate_and_save_cohort_info(
    is_final=False,
    cohort=selected_cohort,
    info_path=json_path,
    is_gene_available=len(genetic_df.columns) > 0,
    is_trait_available=len(clinical_df.columns) > 0
)
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Check directory structure
import os
print("Directory contents:", os.listdir(tcga_root_dir))

# Try loading from the clinical matrix file directly
files = os.listdir(tcga_root_dir)
clinical_files = [f for f in files if 'clinicalmatrix' in f.lower()]

if clinical_files:
    clinical_file_path = os.path.join(tcga_root_dir, clinical_files[0])
    clinical_df = pd.read_csv(clinical_file_path, index_col=0)
    
    # Extract and preview age columns
    age_preview = clinical_df[candidate_age_cols].head(5).to_dict(orient='list')
    print("\nAge columns preview:")
    print(age_preview)

    # Extract and preview gender columns 
    gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict(orient='list')
    print("\nGender columns preview:")
    print(gender_preview)
else:
    print("No clinical data files found in directory")
# No clinical data available, so set both columns to None
age_col = None
gender_col = None

# Print the chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Select appropriate demographic columns
age_col = 'age_at_initial_pathologic_diagnosis'  
gender_col = 'gender'

# 1. Extract and standardize clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)

# 2. Normalize gene symbols in genetic data
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df) 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_genetic_df.to_csv(out_gene_data_file)

# 3. Link clinical and genetic data
linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True)

# 4. Handle missing values 
linked_data = handle_missing_values(linked_data, trait)

# 5. Check for bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Validate and save cohort info
note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=selected_cohort,  # Use selected_cohort instead of hardcoded "TCGA"
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 7. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
    
    # Also save clinical and gene data separately
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)
    
    print(f"Data saved to {out_data_file}")
    print("Shape of final linked data:", linked_data.shape)
else:
    print("Dataset was found to be unusable and was not saved")