p3/preprocess/Head_and_Neck_Cancer/code/GSE184944.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE184944"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE184944"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE184944.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE184944.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE184944.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
# Yes - based on background info this is a gene expression dataset using NanoString technology
is_gene_available = True

# 2.1 Data Availability
# Row 0 contains leukoplakia type info which maps to clinical phenotype:
# PL (proliferative leukoplakia) has higher malignant transformation risk vs LL (localized leukoplakia)
trait_row = 0  

# Age not recorded in characteristics
age_row = None

# Row 2 contains gender info
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert leukoplakia type to binary: 
    1 for PL/PEL (proliferative forms with higher risk)
    0 for LL/EL (localized forms with lower risk)
    """
    val = value.split(': ')[1].strip().upper()
    if val in ['PL', 'PEL']:
        return 1
    elif val in ['LL', 'EL']:
        return 0
    return None

def convert_age(value: str) -> float:
    # Not used since age data unavailable
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for Female, 1 for Male"""
    val = value.split(': ')[1].strip().upper()
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    return None

# 3. Save metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Extract clinical features
if trait_row is not None:
    clinical_features = 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_features))
    
    clinical_features.to_csv(out_clinical_data_file)
# 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 standard human gene symbols (e.g. A2M, ABCB1, ABL1, etc.)
# so no mapping is needed
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)

# Read the processed clinical data file 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Gene expression data from oral leukoplakia study comparing proliferative vs localized types."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 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)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")