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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Sarcoma"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Sarcoma/cohort_info.json"
# 1. Look for directory related to sarcoma
available_cohorts = os.listdir(tcga_root_dir)
selected_dir = 'TCGA_Sarcoma_(SARC)' # Direct match for our target trait
if selected_dir not in available_cohorts:
# Record unavailability and exit
validate_and_save_cohort_info(
is_final=False,
cohort="TCGA",
info_path=json_path,
is_gene_available=False,
is_trait_available=False
)
# Since we need to skip this trait, return empty dataframes to avoid errors in subsequent code
clinical_df = pd.DataFrame()
genetic_df = pd.DataFrame()
else:
# Get the full directory path
cohort_dir = os.path.join(tcga_root_dir, selected_dir)
# 2. Get file paths for clinical and genetic data
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
# 3. 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')
# 4. Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Record data availability
is_gene_available = len(genetic_df.columns) > 0
is_trait_available = len(clinical_df.columns) > 0
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']
# Since we don't have direct file access for preview in this step,
# we'll just define the columns for later use
print(f"Candidate age columns identified: {candidate_age_cols}")
print(f"Candidate gender columns identified: {candidate_gender_cols}")
# Define candidate column names for age and gender information
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}")
# 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") |