p3/preprocess/Head_and_Neck_Cancer/code/GSE148320.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE148320"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE148320"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE148320.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE148320.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE148320.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 series appears to be microarray gene expression data from oral tumor xenografts
# therefore it contains gene expression data
is_gene_available = True 

# 2. Variable Availability and Data Type Conversion

# 2.1 Trait (Diet treatment with palmitic acid (PA)) from key 4 
trait_row = 4  # Diet status available
# "PA-rich diet" should be considered the positive case as it was found to promote metastasis
def convert_trait(x):
    if ':' in str(x):
        val = x.split(':')[1].strip()
        if 'PA-rich' in val:
            return 1
        elif 'control' in val:
            return 0
    return None

# Age/Gender not available - these are xenograft samples
age_row = None
def convert_age(x):
    return None

gender_row = None  
def convert_gender(x):
    return None

# 3. Save metadata - initial filtering
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. Clinical Feature Extraction 
# Since trait_row is not None, we need to extract features
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)

# Preview the extracted features
print(preview_df(clinical_features))

# Save to CSV
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 identifiers appear to be Affymetrix probe IDs rather than human gene symbols
# Affymetrix IDs like "11715100_at" need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first 20 values
preview_dict = preview_df(gene_annotation, n=20)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Extract gene mapping from annotation data
# From previewing the annotation data, we can see that:
# 'ID' column matches the probe IDs in gene expression data
# 'Gene Symbol' column contains the gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply mapping to convert probe-level expression to gene-level expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Save the processed gene data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_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.")