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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Acute_Myeloid_Leukemia"
# Input paths
tcga_root_dir = "../DATA/TCGA"
# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
# 1. Select the relevant subdirectory for acute myeloid leukemia
subdirectory = 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
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', 'days_to_birth']
candidate_gender_cols = ['gender']
# Use TCGA project code LAML instead of full trait name
cohort_dir = os.path.join(tcga_root_dir, "LAML")
if not os.path.exists(cohort_dir):
print(f"Error: Directory not found: {cohort_dir}")
print("Please verify the data directory structure and path configuration.")
else:
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
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:", 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:", gender_preview)
# Build the cohort directory path
cohort_dir = os.path.join(tcga_root_dir, "LAML")
# Get the clinical file path
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
# Read clinical data
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
# Default to None
age_col = None
gender_col = None
# Search for age column - look for common patterns
age_candidates = [col for col in clinical_df.columns if 'age' in col.lower()]
if age_candidates:
# Preview first few values of each candidate
for col in age_candidates:
preview = clinical_df[col].head()
# Check if column has numeric age values after conversion
converted = preview.apply(tcga_convert_age)
if not converted.isna().all():
age_col = col
break
# Search for gender column - look for common patterns
gender_candidates = [col for col in clinical_df.columns if 'gender' in col.lower() or 'sex' in col.lower()]
if gender_candidates:
# Preview first few values of each candidate
for col in gender_candidates:
preview = clinical_df[col].head()
# Check if column has valid gender values after conversion
converted = preview.apply(tcga_convert_gender)
if not converted.isna().all():
gender_col = col
break
# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Select the relevant subdirectory for acute myeloid leukemia
subdirectory = 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
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())
# 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) |