p3/preprocess/Head_and_Neck_Cancer/code/GSE104006.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE104006"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE104006"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE104006.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE104006.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE104006.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. Check gene expression data availability
is_gene_available = True  # Background info mentions "gene expression profiling"

# 2.1 Identify data rows
trait_row = 0  # Disease status in row 0
age_row = 2    # Age in row 2 
gender_row = 3 # Gender/Sex in row 3

# 2.2 Define conversion functions
def convert_trait(value: str) -> int:
    """Convert disease status to binary where Thyroid_carcinoma=1, Non-neoplastic_thyroid=0"""
    if pd.isna(value) or ":" not in value:
        return None
    value = value.split(": ")[1]
    if "carcinoma" in value.lower():
        return 1
    elif "non-neoplastic" in value.lower():
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    if pd.isna(value) or ":" not in value:
        return None
    try:
        return float(value.split(": ")[1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary where F=0, M=1"""
    if pd.isna(value) or ":" not in value:
        return None
    value = value.split(": ")[1].upper()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    return None

# 3. Save metadata with initial filtering
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. Extract clinical features since trait_row is not None
clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

print("Preview of extracted clinical features:")
print(preview_df(clinical_df))

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Since the previous probe ID inspection revealed this is miRNA data, update metadata
is_gene_available = False
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)
)
print("Warning: Dataset contains miRNA data rather than gene expression data.")
print("Further genetic data processing will be skipped.")
genetic_data = None