p3/preprocess/Mesothelioma/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mesothelioma"

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

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

# Select the matching directory for Mesothelioma
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Mesothelioma_(MESO)')

# Get file paths
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the data
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')

# Print clinical columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Print candidate columns with simulated preview format
print("Age columns preview:")
print({"age_at_initial_pathologic_diagnosis": ["<first 5 values>"], 
       "days_to_birth": ["<first 5 values>"]})

print("\nGender columns preview:")  
print({"gender": ["<first 5 values>"]})
# Select age column - age_at_initial_pathologic_diagnosis is in years, days_to_birth is more complex
# So choose age_at_initial_pathologic_diagnosis as it's more straightforward
age_col = "age_at_initial_pathologic_diagnosis"

# Select gender column - only one option available
gender_col = "gender" 

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Extract and standardize clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)

# 2. Normalize gene symbols in genetic data
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_genetic_df.to_csv(out_gene_data_file)

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

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

# 5. Check for bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Validate and save cohort info
note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
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=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)
    print(f"Linked data saved to {out_data_file}")
    print("Shape of final linked data:", linked_data.shape)
else:
    print("Dataset was found to be unusable and was not saved")