p3/preprocess/Kidney_Chromophobe/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Chromophobe"

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

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

# Select the relevant directory
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Kidney_Chromophobe_(KICH)')

# Get paths to clinical and genetic data files
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
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 data columns for review
print("Clinical data columns:", clinical_df.columns.tolist())

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

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

# For this step, just output the identified candidate columns without attempting to preview data
print("Identified candidate demographic columns:")
print(f"Age columns: {candidate_age_cols}")
print(f"Gender columns: {candidate_gender_cols}")
# Choose age and gender columns from candidates
age_candidates = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
gender_candidates = ['gender']

# Select age column - prefer 'age_at_initial_pathologic_diagnosis' over 'days_to_birth'
# since it directly represents age rather than needing conversion
age_col = 'age_at_initial_pathologic_diagnosis' if age_candidates else None

# Select gender column if available
gender_col = 'gender' if gender_candidates else None

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

# Normalize gene symbols and save
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
normalized_genetic_df.to_csv(out_gene_data_file)

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

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

# Judge whether features are biased and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save cohort info
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=trait_biased,
    df=linked_data,
    note=""
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)