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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Bladder_Cancer"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json"
# 1. Select the relevant subdirectory for bladder cancer
subdirectory = 'TCGA_Bladder_Cancer_(BLCA)'
cohort_dir = os.path.join(tcga_root_dir, subdirectory)
# 2. Get the file paths
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())
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'days_to_birth']
candidate_gender_cols = ['gender']
# Print directory contents to check structure
print("TCGA root directory contents:")
print(os.listdir(tcga_root_dir))
# Dictionaries from previous steps showing age and gender column candidates
age_col_samples = {
'age_at_initial_pathologic_diagnosis': [62, 68, 71, 69, 76],
'age': [62, 68, 71, 69, 76]
}
gender_col_samples = {
'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']
}
# Select appropriate columns based on data quality
age_col = 'age_at_initial_pathologic_diagnosis' # More specific clinical column name
gender_col = 'gender' # Only gender column available with valid values
# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Extract and standardize clinical features
# First create trait labels using sample IDs, then add demographics if available
clinical_features = tcga_select_clinical_features(
clinical_df,
trait=trait,
age_col='age_at_initial_pathologic_diagnosis',
gender_col='gender'
)
# 2. Normalize gene symbols and save
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)
# 3. Link clinical and genetic data
linked_data = pd.concat([clinical_features, normalized_gene_df.T], axis=1)
# 4. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 5. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 6. Validate data quality and save cohort info
note = "Contains molecular data from tumor and normal samples with patient demographics."
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=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) |