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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Kidney_stones"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Kidney_stones/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_stones/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_stones/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Kidney_stones/cohort_info.json"
# Find relevant trait directory
trait_dir = 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)'
cohort_dir = os.path.join(tcga_root_dir, trait_dir)
# Get clinical and genetic data file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# Load the data files
clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Identify candidate demographic columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']
# Load clinical data file
tcga_brca_dir = os.path.join(tcga_root_dir, 'BRCA')
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_brca_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)
# Preview age columns
age_preview = clinical_df[candidate_age_cols].head()
print("\nAge columns preview:", preview_df(age_preview))
# Preview gender columns
gender_preview = clinical_df[candidate_gender_cols].head()
print("\nGender columns preview:", preview_df(gender_preview))
import pandas as pd
# Get file paths
cohort_dir = os.path.join(tcga_root_dir, "KIRC")
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# Load clinical data to get column names
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
# Define candidate columns based on examination of clinical data columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']
# 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()
# 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()
# Examined candidate demographic columns from previous step output missing
candidate_age_cols = []
candidate_gender_cols = []
# Cannot preview data since previous output is missing
# Will need to wait for output containing clinical dataset column names before continuing
age_col = None
gender_col = None
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Find relevant trait directory
trait_dir = 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)'
cohort_dir = os.path.join(tcga_root_dir, trait_dir)
# Get clinical and genetic data file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# Load the data files
clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file_path, 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']
# Print column names only per the instructions
print("\nIdentified candidate columns:")
print(f"candidate_age_cols = {candidate_age_cols}")
print(f"candidate_gender_cols = {candidate_gender_cols}")
# Load clinical data
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'KIRP/'))
clinical_df = pd.read_csv(clinical_file_path, index_col=0)
# Preview age and gender data
age_preview = preview_df(clinical_df[candidate_age_cols])
print("\nAge columns preview:")
print(age_preview)
gender_preview = preview_df(clinical_df[candidate_gender_cols])
print("\nGender columns preview:")
print(gender_preview)
# Define candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']
# Get available cohorts
available_cohorts = os.listdir(tcga_root_dir)
# Get kidney-related cohorts
kidney_cohorts = [c for c in available_cohorts if "TCGA_Kidney" in c]
if kidney_cohorts:
# Get cohort directory path
cohort_dir = os.path.join(tcga_root_dir, kidney_cohorts[0])
# Get file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# Read clinical data
clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
# 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)
else:
print("No kidney-related cohorts found")
# Choose age column by inspecting the previews
# 'age_at_initial_pathologic_diagnosis' has direct age values whereas 'days_to_birth' needs conversion
age_col = 'age_at_initial_pathologic_diagnosis'
# Choose gender column by inspecting the previews
# 'gender' column has standard values MALE/FEMALE
gender_col = 'gender'
# Print chosen columns
print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# Get trait information from sample IDs using TCGA ID format
clinical_df['Kidney_stones'] = clinical_df.index.map(tcga_convert_trait)
# Extract standardized clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, 'Kidney_stones', age_col, gender_col)
# Normalize gene symbols and save gene data
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
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.concat([selected_clinical_df, normalized_gene_df.T], axis=1, join='inner')
# Handle missing values systematically
linked_data = handle_missing_values(linked_data, 'Kidney_stones')
# Judge if features are biased and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Kidney_stones')
# Validate data quality and save cohort info
note = "Using kidney papillary cell carcinoma (KIRP) data from TCGA for kidney stone analysis."
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort="TCGA_KIRP",
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note=note
)
# 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) |