Liu-Hy's picture
Add files using upload-large-folder tool
1f52ac2 verified
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Melanoma"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Melanoma/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
# Find melanoma data directory
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)")
# 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 columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']
# Get correct file paths using library function
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "SKCM"))
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
# Preview using library function
age_preview = preview_df(clinical_df[candidate_age_cols])
print("Age columns preview:")
print(age_preview)
gender_preview = preview_df(clinical_df[candidate_gender_cols])
print("\nGender columns preview:")
print(gender_preview)
# Set TCGA cohort name
trait = "SKCM" # TCGA code for Skin Cutaneous Melanoma
# Get file paths
cohort_dir = os.path.join(tcga_root_dir, trait)
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
# Define candidate columns
candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = ["gender"]
# Extract and preview age columns if available
age_preview = {}
if len(candidate_age_cols) > 0:
clinical_data = pd.read_csv(clinical_file_path, index_col=0)
for col in candidate_age_cols:
if col in clinical_data.columns:
age_preview[col] = clinical_data[col].head().tolist()
print("Age Column Preview:", age_preview)
# Extract and preview gender columns if available
gender_preview = {}
if len(candidate_gender_cols) > 0:
if 'clinical_data' not in locals():
clinical_data = pd.read_csv(clinical_file_path, index_col=0)
for col in candidate_gender_cols:
if col in clinical_data.columns:
gender_preview[col] = clinical_data[col].head().tolist()
print("Gender Column Preview:", gender_preview)
# Previous execution output contained dictionaries of age and gender column candidates
# Set chosen column names for demographic information based on that data
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"
# Print the 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_Melanoma_(SKCM)")
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 melanoma 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)