p3/preprocess/Large_B-cell_Lymphoma/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"

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

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

# 1. From the subdirectories list, select Large B-cell Lymphoma (DLBC) data since it matches our target trait
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Large_Bcell_Lymphoma_(DLBC)')

# 2. Get the clinical and genetic data 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())
# First check available directories
import os
print("Available directories:", os.listdir(tcga_root_dir))

# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Large B-cell Lymphoma corresponds to DLBC (Diffuse Large B-Cell Lymphoma) in TCGA nomenclature
cohort_dir = [os.path.join(tcga_root_dir, d) for d in os.listdir(tcga_root_dir) 
              if "DLBC" in d][0]

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

# Read clinical data 
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Extract and 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)

# Extract and 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)
# Get the cohort directory path
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Large_Bcell_Lymphoma_(DLBC)")

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

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

# Extract candidate demographic columns
candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "_age_at_initial_pathologic_diagnosis"] 
candidate_gender_cols = ["gender"]

# Preview candidate columns if they exist in the data
demo_preview = {}

if any(col in clinical_df.columns for col in candidate_age_cols):
    for col in candidate_age_cols:
        if col in clinical_df.columns:
            demo_preview[col] = clinical_df[col].head().tolist()

if any(col in clinical_df.columns for col in candidate_gender_cols):
    for col in candidate_gender_cols:
        if col in clinical_df.columns:
            demo_preview[col] = clinical_df[col].head().tolist()

print("candidate_age_cols =", candidate_age_cols)
print("candidate_gender_cols =", candidate_gender_cols)
print("\nPreview of demographic columns:")
print(demo_preview)
# Store the preview data
preview_dict = {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}

# Check age columns
age_col = None
if candidate_age_cols:
    # Select first age column that has valid age values
    for col in candidate_age_cols:
        if col in preview_dict and any(isinstance(x, (int, float)) or (isinstance(x, str) and str(x).strip().isdigit()) for x in preview_dict[col]):
            age_col = col
            break

# Check gender columns            
gender_col = None
if candidate_gender_cols:
    # Select first gender column that has valid gender values
    for col in candidate_gender_cols:
        if col in preview_dict and any(isinstance(x, str) and str(x).upper() in ['MALE', 'FEMALE'] for x in preview_dict[col]):
            gender_col = col
            break

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Extract and standardize clinical features
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
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')

# Define demographic columns based on inspection from previous steps
age_col = 'age_at_initial_pathologic_diagnosis'
gender_col = 'gender'

# Create a DataFrame with just the sample IDs to ensure proper trait encoding
sample_ids = pd.DataFrame(index=genetic_df.columns)
selected_clinical_df = tcga_select_clinical_features(sample_ids, trait, age_col=None, gender_col=None)

# Add age and gender from clinical data if available
if age_col in clinical_df.columns:
    selected_clinical_df['Age'] = clinical_df[age_col]
if gender_col in clinical_df.columns:
    selected_clinical_df['Gender'] = clinical_df[gender_col].apply(tcga_convert_gender)

# 2. 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)

# 3. Link clinical and genetic data
linked_data = pd.concat([selected_clinical_df, normalized_gene_df.T], axis=1)

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

# 5. Check for biased features and remove biased demographic features
is_trait_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Validate data quality and save cohort info
note = "Data from TCGA Large B-cell Lymphoma (DLBC) cohort. Classification based on TCGA sample type codes (01-09: tumor, 10-19: normal)."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA_DLBC",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_trait_biased,
    df=cleaned_data,
    note=note
)

# 7. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    cleaned_data.to_csv(out_data_file)
    print(f"Data saved to {out_data_file}")
else:
    print("Data quality validation failed. Dataset not saved.")