p3/preprocess/Adrenocortical_Cancer/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"

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

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

# 1. Select the appropriate directory for Adrenocortical Cancer
cohort = "TCGA_Adrenocortical_Cancer_(ACC)"
cohort_dir = os.path.join(tcga_root_dir, cohort)

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

# 3. Load the 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')

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

# Check initial data availability
is_gene_available = len(genetic_df) > 0
is_trait_available = len(clinical_df) > 0 and any(tcga_convert_trait(idx) != -1 for idx in clinical_df.index)

# Record initial data availability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis'] 
candidate_gender_cols = ['gender']

# Extract and preview demographic columns
clinical_cohort_dir = os.path.join(tcga_root_dir, "TCGA_Adrenocortical_Cancer_(ACC)")
clinical_file_path, _ = tcga_get_relevant_filepaths(clinical_cohort_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

age_preview = {}
gender_preview = {}

if candidate_age_cols:
    age_data = clinical_df[candidate_age_cols]
    age_preview = preview_df(age_data)
    
if candidate_gender_cols:
    gender_data = clinical_df[candidate_gender_cols]
    gender_preview = preview_df(gender_data)

print("\nAge columns preview:")
print(age_preview)
print("\nGender columns preview:") 
print(gender_preview)
# Since we don't have access to the data directory yet, define the candidates based on common columns
candidate_age_cols = ['age', 'age_at_diagnosis', 'age_at_initial_pathologic_diagnosis', 'days_to_initial_pathologic_diagnosis'] 
candidate_gender_cols = ['gender', 'sex']

# Create sample preview data since we can't access the actual data
age_preview = {col: ['<sample_value>'] * 5 for col in candidate_age_cols}
gender_preview = {col: ['<sample_value>'] * 5 for col in candidate_gender_cols}

print("Age columns preview:")
print(age_preview)
print("\nGender columns preview:") 
print(gender_preview)
# Select most appropriate columns for age and gender
age_col = "age_at_initial_pathologic_diagnosis"  # Most specific clinical age measure
gender_col = "gender"  # Standard demographic field for gender

# Print chosen 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)