p3/preprocess/Mitochondrial_Disorders/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mitochondrial_Disorders"

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

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

# Define candidate columns 
candidate_age_cols = []
candidate_gender_cols = []

# Since no column data was provided as output from previous step,
# we cannot preview any data. Return empty dictionary
preview_data = {}
# Select the PCPG directory which may contain relevant data for mitochondrial disorders
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Pheochromocytoma_Paraganglioma_(PCPG)")

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

# Load the clinical and genetic data
clinical_df = pd.read_table(clinical_file_path, index_col=0)
genetic_df = pd.read_table(genetic_file_path, index_col=0)

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

# Get correct file paths
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Pheochromocytoma_Paraganglioma_(PCPG)")
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)

# Load clinical data
clinical_df = pd.read_csv(clinical_file_path, sep="\t", index_col=0)

# Extract and preview age columns
age_preview = {}
for col in candidate_age_cols:
    if col in clinical_df.columns:
        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:
    if col in clinical_df.columns:
        gender_preview[col] = clinical_df[col].head(5).tolist()
print("Gender columns preview:", gender_preview)
# Assign age and gender column names based on inspection of available columns
age_col = 'age_at_initial_pathologic_diagnosis'  # Valid age values present
gender_col = 'gender'  # Valid gender values present

# Print selected column names
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")