p3/preprocess/Head_and_Neck_Cancer/code/GSE201777.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE201777"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE201777"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE201777.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE201777.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE201777.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, this dataset contains gene expression data as it studies differentially expressed genes 
# in laryngeal cancer tissue samples
is_gene_available = True

# 2. Variable Availability and Data Row Identification
# trait (lymph node metastasis status) is available in row 2
trait_row = 2

# age is not provided in sample characteristics
age_row = None 

# gender is not provided in sample characteristics
gender_row = None

# Conversion functions
def convert_trait(value: str) -> Optional[int]:
    """Convert lymph node metastasis status to binary
    positive -> 1
    negative -> 0
    """
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'positive' in value or 'positiive' in value:
        return 1
    elif 'negative' in value:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to float"""
    return None  # Not used since age data not available

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary"""
    return None  # Not used since gender data not available

# 3. Save metadata with 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. Extract clinical features if trait data is available
if trait_row is not None:
    clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait)
    preview_df(clinical_df)  # Preview the extracted features
    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 appear to be probe set IDs from Affymetrix microarray
# They need to be mapped to gene symbols for standardization and interpretability
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)
# 1. Identify columns containing probe IDs and gene symbols
# From previewing the annotation data, 'ID' contains probe IDs and 'Gene Symbol' contains gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Preview the first few rows and columns
print("\nFirst few rows and columns of mapped gene expression data:")
print(gene_data.iloc[:5, :5])
# 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 glucocorticoid sensitivity study."
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.")