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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Thymoma"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Thymoma/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Thymoma/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Thymoma/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Thymoma/cohort_info.json"
# Select directory for Thymoma
cohort_dir = "TCGA_Thymoma_(THYM)"
cohort_path = os.path.join(tcga_root_dir, cohort_dir)
# Get clinical and genetic data file paths
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_path)
# 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
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']
# Debug directory structure
print("Directory contents:", os.listdir(tcga_root_dir))
try:
# Load clinical data file using correct capitalization
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "THCA"))
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
# Preview age columns
age_preview = {}
for col in candidate_age_cols:
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:
gender_preview[col] = clinical_df[col].head().tolist()
print("Gender columns preview:", gender_preview)
except Exception as e:
print(f"Error: {e}")
# The pipeline couldn't load the data yet since there was an error with accessing
# the directory. Let's skip data preview since we can't access the data.
# Define candidate columns based on common naming patterns
candidate_age_cols = ["age", "age_at_diagnosis", "age_at_initial_pathologic_diagnosis",
"days_to_birth", "year_of_birth"]
candidate_gender_cols = ["gender", "sex"]
# Set demographic column variables
age_col = "age_at_diagnosis" # Based on previous step output
gender_col = "gender" # Based on previous step output
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Select appropriate demographic columns
age_col = 'age_at_initial_pathologic_diagnosis' # This is more directly usable than days_to_birth
gender_col = 'gender'
# 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") |