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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE156915"
# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE156915"
# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE156915.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE156915.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE156915.csv"
json_path = "./output/preprocess/3/Head_and_Neck_Cancer/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data Availability
# The dataset contains whole transcriptome data which includes gene expression
is_gene_available = True
# 2. Variable Availability and Row Numbers
# No explicit head and neck cancer trait information available in sample characteristics
trait_row = None
convert_trait = None
# Age information not available
age_row = None
convert_age = None
# Gender information not available
gender_row = None
convert_gender = None
# 3. Save metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=trait_row is not None
)
# 4. Skip clinical feature extraction since trait_row is None
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These appear to be human gene symbols with some special cases
# The format "1060P11.3 /// KIR3DP1" uses /// to separate aliases/alternative names
# Most entries like A1BG, A1BG-AS1, A1CF etc. are standard HUGO gene symbols
# No mapping needed as they are already in gene symbol format
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# No need to do data linking and bias checking since trait data is missing
dummy_data = pd.DataFrame([[0]], columns=['dummy'])
is_biased = True # Dataset is biased by definition when trait data is missing
note = "Dataset contains gene expression data but lacks trait information needed for association studies."
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=is_biased,
df=dummy_data,
note=note
)
print(f"Dataset {cohort} contains no trait information and will not be used for analysis.") |