p3/preprocess/Metabolic_Rate/code/GSE151683.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Metabolic_Rate"
cohort = "GSE151683"

# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE151683"

# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE151683.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE151683.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE151683.csv"
json_path = "./output/preprocess/3/Metabolic_Rate/cohort_info.json"

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

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info mentioning DNA microarray analysis and gene expression profiles
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Identify rows for each variable 
# Trait (Metabolic Rate) can be inferred from treatment group and time
trait_row = 4  # time weeks used to calculate metabolic rate change
gender_row = 1  # Gender data available
age_row = 2  # Age data available

# 2.2 Conversion functions
def convert_trait(value: str) -> Optional[float]:
    """Convert time weeks to binary - 0 for baseline, 1 for endpoint"""
    if not value:
        return None
    try:
        time = float(value.split(': ')[1])
        if time == 0:
            return 0.0
        elif time == 8:
            return 1.0
        return None
    except:
        return None

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

def convert_gender(value: str) -> Optional[float]:
    """Convert gender to binary - 0 for female, 1 for male"""
    if not value:
        return None
    gender = value.split(': ')[1].lower()
    if gender == 'male':
        return 1.0
    elif gender == 'female':
        return 0.0
    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 = 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 data
preview_data = preview_df(selected_clinical)
print("Preview of clinical data:")
print(preview_data)

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# These identifiers are Affymetrix probe IDs (ending in "_at" or "_s_at"), 
# not standard human gene symbols.
# They need to be mapped to gene symbols for consistency and interpretability.
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Save processed gene expression data
gene_data.to_csv(out_gene_data_file)

# Preview results
print("Preview of mapped gene expression data:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)