p3/preprocess/Head_and_Neck_Cancer/code/GSE151179.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE151179"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE151179"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE151179.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE151179.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE151179.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 - Series title and description indicate gene expression data using Human Clariom S Assay
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability 
# Trait (RAI response) is recorded in row 4
trait_row = 4
# Age and gender are not recorded in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion
def convert_trait(value: str) -> int:
    """Convert RAI response to binary (0=Avid, 1=Refractory)"""
    if not value or 'patient rai responce:' not in value:
        return None
    value = value.split(': ')[1].strip()
    if value == 'Avid':
        return 0
    elif value == 'Refractory':
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary"""
    return 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. Clinical Feature Extraction
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))

clinical_df.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 look like probe/transcript IDs from microarray/sequencing platforms,
# not standard human gene symbols which are typically alphabetic like "BRCA1"
# The numerical format indicates these need to be mapped to 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)
# The IDs in gene expression data look like numbers (e.g. 23064070)
# In gene annotation, 'ID' column has the matching identifiers 
# The SPOT_ID.1 column contains gene info with symbols
prob_col = 'ID'
gene_col = 'SPOT_ID.1'

# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

# Convert probe expression to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Save the 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.")