p3/preprocess/Glucocorticoid_Sensitivity/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glucocorticoid_Sensitivity"

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

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

# Select the ACC cohort since it's relevant for glucocorticoid sensitivity
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Adrenocortical_Cancer_(ACC)")

# Get file paths for clinical and genetic data
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# 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')

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

# Check data availability
is_gene_available = len(genetic_df) > 0
is_trait_available = len(clinical_df) > 0

# Validate initial data availability
validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA",
    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', 'days_to_birth']
candidate_gender_cols = ['gender']

# Get clinical data
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Adrenocortical_Cancer_(ACC)')
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
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().tolist()
print("Age columns preview:")
print(age_preview)

# Extract and preview gender columns  
gender_preview = {}
for col in candidate_gender_cols:
    gender_preview[col] = clinical_df[col].head().tolist()
print("\nGender columns preview:")  
print(gender_preview)
# Define candidate demographic columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Create previews if any candidates exist  
age_preview = {}
if candidate_age_cols:
    clinical_age_df = clinical_df[candidate_age_cols]
    age_preview = preview_df(clinical_age_df)
print("Age column preview:", age_preview)

gender_preview = {}
if candidate_gender_cols:
    clinical_gender_df = clinical_df[candidate_gender_cols]
    gender_preview = preview_df(clinical_gender_df)
print("Gender column preview:", gender_preview)
# Select age and gender columns from the candidates
age_col = 'age_at_initial_pathologic_diagnosis'  # Contains actual age values
gender_col = 'gender'  # Contains clear gender labels

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Get variables from previous step
age_col = 'age_at_initial_pathologic_diagnosis'
gender_col = 'gender'

# 1. Extract and standardize clinical features
clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)

# 2. Normalize gene expression 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(normalized_genetic_df.T, clinical_df, left_index=True, right_index=True)

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

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

# 6. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=len(normalized_genetic_df.columns) > 0,
    is_trait_available=trait in linked_data.columns,
    is_biased=is_biased,
    df=linked_data,
    note="Data from TCGA Adrenocortical Cancer (ACC) cohort"
)

# 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)