p3/preprocess/Heart_rate/code/GSE72462.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Heart_rate"
cohort = "GSE72462"

# Input paths
in_trait_dir = "../DATA/GEO/Heart_rate"
in_cohort_dir = "../DATA/GEO/Heart_rate/GSE72462"

# Output paths
out_data_file = "./output/preprocess/3/Heart_rate/GSE72462.csv"
out_gene_data_file = "./output/preprocess/3/Heart_rate/gene_data/GSE72462.csv"
out_clinical_data_file = "./output/preprocess/3/Heart_rate/clinical_data/GSE72462.csv"
json_path = "./output/preprocess/3/Heart_rate/cohort_info.json"

# Get file paths
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_path)

# Get unique values by row in clinical data and limit the number shown
sample_chars = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in sample_chars.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
is_gene_available = True  # Background info mentions "gene expression microarray analysis"

# 2.1 Data Availability
trait_row = 0  # Insulin sensitivity response status as proxy for heart rate regulation
age_row = 3   # Age
gender_row = 2 # Sex

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert insulin sensitivity response to binary.
    0: non-responder (worse heart rate regulation)
    1: responder (better heart rate regulation)
    """
    if not isinstance(value, str):
        return None
    value = value.split(': ')[1].lower() if ': ' in value else value.lower()
    if 'non-responder' in value:
        return 0
    elif 'responder' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to continuous value."""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[1] if ': ' in value else value
    try:
        return float(value)
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary.
    0: female
    1: male
    """
    if not isinstance(value, str):
        return None
    value = value.split(': ')[1].lower() if ': ' in value else value.lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        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. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        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
    preview = preview_df(clinical_features)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
gene_data = get_genetic_data(matrix_path)

# Print first 20 probe/gene IDs
print("First 20 probe/gene IDs:")
print(gene_data.index[:20].tolist())
# These identifiers look like Affymetrix probe IDs ('_st' suffix is characteristic of Affymetrix arrays)
# They need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_path)

# Preview column names and first few values
column_preview = preview_df(gene_annotation)
print("\nGene annotation columns and sample values:")
print(column_preview)
# From the preview, ID column in gene_annotation matches the probe IDs in gene expression data
# gene_assignment contains gene symbols along with other info
prob_col = 'ID'
gene_col = 'gene_assignment'

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

# Apply mapping and convert probe measurements to gene expression values
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the gene expression data
print("\nFirst 10 genes and their expression values:")
print(gene_data.head(10))
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Check for biases and remove biased demographic features
trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'
if trait_type == "binary":
    is_biased = judge_binary_variable_biased(linked_data, trait)
else:
    is_biased = judge_continuous_variable_biased(linked_data, trait)

# Remove biased demographic features
if "Age" in linked_data.columns:
    if judge_continuous_variable_biased(linked_data, "Age"):
        linked_data = linked_data.drop(columns="Age")
if "Gender" in linked_data.columns:
    if judge_binary_variable_biased(linked_data, "Gender"):
        linked_data = linked_data.drop(columns="Gender")

# 5. Validate and save cohort info
note = "The dataset contains before/after exercise measurements for each subject. We merged them to increase statistical power."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)