p3/preprocess/Acute_Myeloid_Leukemia/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"

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

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

# 1. Select the relevant subdirectory for acute myeloid leukemia
subdirectory = 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
cohort_dir = os.path.join(tcga_root_dir, subdirectory)

# 2. Get the file paths 
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# 3. Load the data files
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())
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Use TCGA project code LAML instead of full trait name
cohort_dir = os.path.join(tcga_root_dir, "LAML")
if not os.path.exists(cohort_dir):
    print(f"Error: Directory not found: {cohort_dir}")
    print("Please verify the data directory structure and path configuration.")
else:
    clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
    clinical_df = pd.read_csv(clinical_file_path, index_col=0)
    
    # Preview age columns 
    age_preview = {}
    for col in candidate_age_cols:
        age_preview[col] = clinical_df[col].head(5).tolist()
    print("Age columns preview:", age_preview)
    
    # Preview gender columns
    gender_preview = {}
    for col in candidate_gender_cols:
        gender_preview[col] = clinical_df[col].head(5).tolist()
    print("\nGender columns preview:", gender_preview)
# Build the cohort directory path
cohort_dir = os.path.join(tcga_root_dir, "LAML")

# Get the clinical file path
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)

# Read clinical data
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)

# Default to None 
age_col = None 
gender_col = None

# Search for age column - look for common patterns
age_candidates = [col for col in clinical_df.columns if 'age' in col.lower()]
if age_candidates:
    # Preview first few values of each candidate
    for col in age_candidates:
        preview = clinical_df[col].head()
        # Check if column has numeric age values after conversion
        converted = preview.apply(tcga_convert_age)
        if not converted.isna().all():
            age_col = col
            break

# Search for gender column - look for common patterns  
gender_candidates = [col for col in clinical_df.columns if 'gender' in col.lower() or 'sex' in col.lower()]
if gender_candidates:
    # Preview first few values of each candidate
    for col in gender_candidates:
        preview = clinical_df[col].head()
        # Check if column has valid gender values after conversion
        converted = preview.apply(tcga_convert_gender)
        if not converted.isna().all():
            gender_col = col
            break

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Select the relevant subdirectory for acute myeloid leukemia
subdirectory = 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
cohort_dir = os.path.join(tcga_root_dir, subdirectory)

# 2. Get the file paths 
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# 3. Load the data files
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())
# 1. Extract and standardize clinical features
# First create trait labels using sample IDs, then add demographics if available
clinical_features = tcga_select_clinical_features(
    clinical_df, 
    trait=trait,
    age_col='age_at_initial_pathologic_diagnosis',
    gender_col='gender'
)

# 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
linked_data = pd.concat([clinical_features, normalized_gene_df.T], axis=1)

# 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)