p3/preprocess/Obesity/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"

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

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

# 1. We'll use TCGA_Breast_Cancer as obesity is a known risk factor for breast cancer
cohort = "TCGA_Breast_Cancer_(BRCA)"
cohort_dir = os.path.join(tcga_root_dir, cohort)

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

# 3. Load the data
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())

# Check initial data availability
is_gene_available = len(genetic_df) > 0  
is_trait_available = 'BMI' in clinical_df.columns or any('bmi' in col.lower() for col in clinical_df.columns)

# Record initial data availability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)
# 1. Identify candidate demographic columns
candidate_age_cols = ["Age_at_Initial_Pathologic_Diagnosis_nature2012", "age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = ["Gender_nature2012", "gender"]

# 2. Preview the column data
# Read in clinical data directly from pre-loaded DataFrame clinical_df
age_preview = clinical_df[candidate_age_cols].head(5).to_dict(orient='list')
print("Age columns preview:", age_preview)

# Extract and preview gender columns
gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict(orient='list')
print("Gender columns preview:", gender_preview)
# Select age column based on preview data - age_at_initial_pathologic_diagnosis has valid numeric values
age_col = "age_at_initial_pathologic_diagnosis"

# Select gender column based on preview data - gender has valid string values 
gender_col = "gender"

# Print chosen columns
print("Selected age column:", age_col)
print("Selected gender column:", gender_col)
# 1. Extract and standardize clinical features
# Create a new dataframe to store sample ID as trait with same dimensions as genetic data
sample_df = pd.DataFrame(index=genetic_df.columns)
sample_df[trait] = -1  # Fill with invalid values since trait unavailable
clinical_features = sample_df

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)

# 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 on sample IDs
linked_data = pd.merge(
    clinical_features, 
    normalized_gene_df.T,
    left_index=True,
    right_index=True,
    how='inner'
)

# 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 = "Dataset contains gene expression data but lacks obesity/BMI information in clinical data. All samples marked with invalid trait values."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 7. Skip saving linked data since trait unavailable, making dataset unusable