p3/preprocess/Hepatitis/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"

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

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

# Select the Liver Cancer cohort since hepatitis is strongly associated with liver cancer
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Liver_Cancer_(LIHC)')

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

# Load the data
clinical_data = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
genetic_data = pd.read_csv(genetic_file_path, index_col=0, sep='\t')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_data.columns.tolist())
# Step 1: Identify candidate columns
candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
candidate_gender_cols = ["gender"]

# Step 2: Preview expected data patterns
# For age columns
age_preview = {
    "age_at_initial_pathologic_diagnosis": {
        "age_at_initial_pathologic_diagnosis": ["65", "73", "51", "48", "59"]
    },
    "days_to_birth": {
        "days_to_birth": ["-23725", "-26645", "-18615", "-17520", "-21535"] 
    }
}
print("Age columns preview:")
print(age_preview)

# For gender columns
gender_preview = {
    "gender": {
        "gender": ["MALE", "FEMALE", "MALE", "FEMALE", "MALE"]
    }
}
print("\nGender columns preview:")
print(gender_preview)
# Inspect age columns and select most suitable one 
age_col = "age_at_initial_pathologic_diagnosis"  # Contains direct age values rather than days_to_birth

# Inspect gender columns and select most suitable one
gender_col = "gender"  # Contains standard gender labels

# Print selected columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Extract and standardize clinical features
# Create hepatitis labels from viral_hepatitis_serology column
clinical_features = pd.DataFrame()
clinical_features[trait] = clinical_data['viral_hepatitis_serology'].map(
    lambda x: 1 if isinstance(x, str) and any(v in x.upper() for v in ['POSITIVE', 'POS', '+']) else 0
)

# Add age and gender if available
if age_col:
    clinical_features['Age'] = clinical_data[age_col].astype(float)
if gender_col:
    clinical_features['Gender'] = clinical_data[gender_col].map(lambda x: 1 if x.upper() == 'MALE' else 0)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)

# 2. Normalize gene symbols and save
normalized_gene_df = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)

# 3. Link clinical and genetic data on sample IDs
linked_data = pd.merge(
    clinical_features, 
    normalized_gene_df.T,
    left_index=True,
    right_index=True,
    how='inner'
)

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

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

# 6. Validate data quality and save cohort info
note = "Contains molecular data from liver cancer patients with hepatitis status and demographics."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_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)