p3/preprocess/Head_and_Neck_Cancer/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"

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

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

# Find the Head and Neck Cancer cohort directory
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Head_and_Neck_Cancer_(HNSC)')

# Get paths for clinical and genetic data files
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the clinical and genetic data
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')

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

is_gene_available = len(genetic_df.columns) > 0
is_trait_available = len(clinical_df.columns) > 0

# Record initial validation results
validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA", 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)
# Define candidate columns from examining the clinical data columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']

# Since we've completed part 1 of identifying candidate columns,
# Print the identified columns to complete this step
print("Candidate age columns:", candidate_age_cols)
print("Candidate gender columns:", candidate_gender_cols)
# Select columns for age and gender information
age_col = 'age_at_initial_pathologic_diagnosis' if 'age_at_initial_pathologic_diagnosis' in ['age_at_initial_pathologic_diagnosis'] else None
gender_col = 'gender' if 'gender' in ['gender'] else None

# Print chosen column names
print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# 1. Extract and standardize clinical features
clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)

# 2. Normalize gene expression 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(normalized_genetic_df.T, clinical_df, left_index=True, right_index=True)

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

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

# 6. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=len(normalized_genetic_df.columns) > 0,
    is_trait_available=trait in linked_data.columns,
    is_biased=is_biased,
    df=linked_data,
    note="Data from TCGA Head and Neck Cancer (HNSC) cohort"
)

# 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)