p3/preprocess/Longevity/code/GSE48264.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Longevity"
cohort = "GSE48264"

# Input paths
in_trait_dir = "../DATA/GEO/Longevity"
in_cohort_dir = "../DATA/GEO/Longevity/GSE48264"

# Output paths
out_data_file = "./output/preprocess/3/Longevity/GSE48264.csv"
out_gene_data_file = "./output/preprocess/3/Longevity/gene_data/GSE48264.csv"
out_clinical_data_file = "./output/preprocess/3/Longevity/clinical_data/GSE48264.csv"
json_path = "./output/preprocess/3/Longevity/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Affymetrix gene-chips mentioned in background info

# 2. Variable Availability and Row Identification
trait_row = 3  # Survival status recorded in row 3
age_row = None  # Age is constant at 70 years for all subjects
gender_row = None  # Gender data not available

# Define conversion functions
def convert_trait(value: str) -> Optional[int]:
    """Convert survival status to binary (0=alive, 1=deceased)"""
    if not value or ":" not in value:
        return None
    status = value.split(":")[1].strip()
    if status == "Death":
        return 1
    elif status == "None":  # Still alive
        return 0
    elif status == "Hosp":  # Hospitalized but not deceased
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Not used since age is constant"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Not used since gender data unavailable"""
    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. Extract clinical features
selected_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
)

# Preview and save clinical data
print(preview_df(selected_clinical_df))
selected_clinical_df.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# These numbers appear to be probe IDs, not standard human gene symbols
# Human gene symbols typically follow patterns like BRCA1, TP53, IL6, etc.
# This data seems to use numeric probe identifiers that will need to be mapped to gene symbols
requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))
# 1. The 'ID' column in gene annotation matches probe IDs in gene expression data
# The 'gene_assignment' contains gene symbol information

# 2. Extract mapping between probe IDs and gene symbols
def extract_first_gene_symbol(text: str) -> str:
    """Extract first gene symbol from gene_assignment string"""
    if text == '---' or pd.isna(text):
        return None
    # The format is typically: "RefSeq // GENE_SYMBOL // description"
    # First split by '//' and take second item which contains gene symbol
    parts = text.split('//')
    if len(parts) >= 2:
        return parts[1].strip()
    return None

mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col='ID',
    gene_col='gene_assignment'
)
mapping_df['Gene'] = mapping_df['Gene'].apply(extract_first_gene_symbol)
mapping_df = mapping_df.dropna()

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

# Preview results
print("\nFirst few rows and columns of gene expression data:")
print(gene_data.iloc[:5, :5])
# 1. Normalize gene symbols and save 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)

# 2. Link clinical and genetic data
selected_clinical_df = selected_clinical_df.rename(index={0: 'Longevity'})
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, 'Longevity')

# 4. Check for biased features and remove biased demographic ones
is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Longevity')

# 5. Final validation and save metadata
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="All subjects are male according to series summary. Age information not available."
)

# 6. 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)