p3/preprocess/Thyroid_Cancer/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Thyroid_Cancer"

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

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

# Find thyroid cancer data directory 
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Thyroid_Cancer_(THCA)")

# Get paths to clinical and genetic data files
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')

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

# Mark data as available
is_gene_available = True 
is_trait_available = True
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
)
# 1. Identify candidate columns 
candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
candidate_gender_cols = ["gender"]

# 2. Preview the candidate columns
age_df = clinical_df[candidate_age_cols]
gender_df = clinical_df[candidate_gender_cols]

print("Age columns preview:")
print(preview_df(age_df))

print("\nGender columns preview:")  
print(preview_df(gender_df))
# Analyze age columns
# Both columns contain valid age information, but age_at_initial_pathologic_diagnosis is more intuitive
age_col = "age_at_initial_pathologic_diagnosis"

# Analyze gender columns
# gender column contains valid gender information
gender_col = "gender"

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Get paths
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Thyroid_Cancer_(THCA)")
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)

# Load data
clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') 
genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')

# Extract clinical features
selected_clinical_df = tcga_select_clinical_features(
    clinical_df=clinical_df,
    trait=trait,
    age_col=age_col,
    gender_col=gender_col
)

# Normalize gene symbols
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)

# Save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)

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

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

# Check for bias and remove biased demographic features
is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save metadata
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=is_biased,
    df=cleaned_data,
    note="This dataset contains TCGA thyroid cancer data. However, it only contains positive cases (all samples are cancer cases), making it unsuitable for phenotype prediction."
)

# Save processed data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    cleaned_data.to_csv(out_data_file)