p3/preprocess/Ocular_Melanomas/code/TCGA.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ocular_Melanomas"

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

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

# 1. Select the directory for Ocular Melanomas
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Ocular_melanomas_(UVM)')

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

# 3. Load both files as pandas dataframes
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0)

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

# Mark data as available
is_gene_available = True if len(genetic_df) > 0 else False
is_trait_available = True if len(clinical_df) > 0 else False

# Initial validation
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 demographic columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Get clinical data path 
clinical_file_path = os.path.join(tcga_root_dir, "UVM", "UVM.GDC_phenotype.tsv")

# Read clinical data
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)

# Preview age columns
age_preview = {}
for col in candidate_age_cols:
    if col in clinical_df.columns:
        age_preview[col] = clinical_df[col].head().tolist()
print("Age columns preview:", age_preview)

# Preview gender columns  
gender_preview = {}
for col in candidate_gender_cols:
    if col in clinical_df.columns:
        gender_preview[col] = clinical_df[col].head().tolist()
print("Gender columns preview:", gender_preview)
# Define candidate demographic columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']

print("\nCandidate age columns:", candidate_age_cols)
print("Candidate gender columns:", candidate_gender_cols)

# Preview the data
if len(candidate_age_cols) > 0:
    age_data = clinical_df[candidate_age_cols]
    print("\nAge column previews:")
    print(preview_df(age_data))

if len(candidate_gender_cols) > 0:
    gender_data = clinical_df[candidate_gender_cols]
    print("\nGender column previews:")
    print(preview_df(gender_data))
# Select the age column - age_at_initial_pathologic_diagnosis has valid numerical values
age_col = 'age_at_initial_pathologic_diagnosis'

# Select the gender column - gender has valid gender values 
gender_col = 'gender'

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Re-define demographic columns
age_col = 'age_at_initial_pathologic_diagnosis'
gender_col = 'gender'

# 1. Extract and standardize clinical features 
trait_col = trait.replace("_", " ")
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait_col, 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)

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

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

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

# 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")