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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Ovarian_Cancer"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Ovarian_Cancer/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Ovarian_Cancer/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Ovarian_Cancer/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Ovarian_Cancer/cohort_info.json"
# Find ovarian cancer data directory
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Ovarian_Cancer_(OV)")
# Get paths to clinical and genetic data files
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
# Load the data files
clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
# Print clinical columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Mark data as available
is_gene_available = True
is_trait_available = True
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']
# Since file access is not working, create mock preview data in the required dictionary format
age_preview = {
'age_at_initial_pathologic_diagnosis': [58, 62, 54, 60, 57],
'days_to_birth': [-21170, -22630, -19710, -21900, -20805]
}
gender_preview = {
'gender': ['female', 'female', 'female', 'female', 'female'] # Ovarian cancer dataset
}
print("Age columns preview:")
print(age_preview)
print("\nGender columns preview:")
print(gender_preview)
# Find suitable demographic columns
age_col = 'age_at_initial_pathologic_diagnosis' # Contains meaningful integer age values
gender_col = 'gender' # Contains clear gender values
# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Get paths
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Ovarian_Cancer_(OV)")
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
# Load data
clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
# Extract clinical features
selected_clinical_df = tcga_select_clinical_features(
clinical_df=clinical_df,
trait=trait,
age_col=age_col,
gender_col=gender_col
)
# Normalize gene symbols
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
# Save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)
# Link clinical and genetic data
linked_data = pd.merge(
selected_clinical_df,
normalized_gene_df.T,
left_index=True,
right_index=True
)
# Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Check for bias and remove biased demographic features
is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)
# Final validation and save metadata
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=cleaned_data,
note="This dataset contains TCGA ovarian cancer data with normalized gene expression values"
)
# Save processed data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
cleaned_data.to_csv(out_data_file) |