p3/preprocess/Arrhythmia/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"

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

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

# TCGA-THCA (Thyroid Cancer) is most relevant for arrhythmia as thyroid dysfunction 
# often causes cardiac arrhythmias

# Set cohort directory
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Thyroid_Cancer_(THCA)")

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

# Load data
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 columns for analysis
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# 1. Identify candidate columns 
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Print the identified columns
print("Candidate age columns:", candidate_age_cols)
print("Candidate gender columns:", candidate_gender_cols)
# Select suitable columns for age and gender
age_col = 'age_at_initial_pathologic_diagnosis'
gender_col = 'gender'

# Print selected columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Extract and standardize clinical features
# Create trait labels from sample IDs (01-09: tumor=1, 10-19: normal=0)
clinical_features = tcga_select_clinical_features(
    clinical_df, 
    trait=trait,
    age_col='age_at_initial_pathologic_diagnosis',
    gender_col='gender'
)
# 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_df)
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 tumor and normal samples with patient 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)