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Datasets:
Liu-Hy
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GenoTEX

Tasks:
Text Generation
Table Question Answering
Tabular Regression
Languages:
English
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100B<n<1T
ArXiv:
Tags:
ai4science
agent
llm
genomics
biology
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GenoTEX
File size: 4,586 Bytes 
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# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"

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

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

# Select the relevant directory
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)')

# Get paths to clinical and genetic data files
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# 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 for review
print("Clinical data columns:", clinical_df.columns.tolist())

# Check data availability 
is_gene_available = len(genetic_df) > 0
is_trait_available = len(clinical_df) > 0

# Record data availability
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 clinical data file path 
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, trait))

# Read clinical data
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Preview age columns
age_preview = {}
for col in candidate_age_cols:
    age_preview[col] = clinical_df[col].head(5).tolist()
print("Age columns preview:")
print(age_preview)

# Preview gender columns
gender_preview = {}
for col in candidate_gender_cols:
    gender_preview[col] = clinical_df[col].head(5).tolist()
print("\nGender columns preview:")
print(gender_preview)
# Select the relevant directory
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)')

# Get paths to clinical and genetic data files
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# 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 for review
print("Clinical data columns:", clinical_df.columns.tolist())

# Check data availability 
is_gene_available = len(genetic_df) > 0
is_trait_available = len(clinical_df) > 0

# Record data availability
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)
# From the clinical columns, identify suitable columns for age and gender
age_col = "age_at_initial_pathologic_diagnosis"  # Most relevant and direct age column
gender_col = "gender"  # Direct gender information column

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

# Extract and standardize clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
selected_clinical_df.to_csv(out_clinical_data_file)

# Normalize gene symbols and save 
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
normalized_genetic_df.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Judge whether features are biased and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save cohort info
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=trait_biased,
    df=linked_data,
    note="TCGA kidney clear cell carcinoma data comparing tumor vs normal tissue samples"
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)