p3/preprocess/Metabolic_Rate/code/GSE40589.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Metabolic_Rate"
cohort = "GSE40589"

# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE40589"

# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE40589.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE40589.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE40589.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
is_gene_available = True  # Yes, based on Series_title and Series_summary describing gene expression in adipose tissue

# 2. Variable Availability and Row Identification
trait_row = None  # Metabolic rate not available in sample characteristics
age_row = None   # Age not available
gender_row = None  # Gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None  # Not used since trait data not available

def convert_age(x):
    return None  # Not used since age data not available 

def convert_gender(x): 
    return None  # Not used since gender data not available

# 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
# Skip since trait_row is None
# 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 start with 'A_23' indicating they are Agilent array probe IDs
# They need to be mapped to human gene symbols for proper analysis
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))
# Create mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Print first few rows and shape to verify mapping worked
print("Gene expression data after mapping:")
print(gene_data.head())
print("\nShape after mapping:", gene_data.shape)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# Since we lack clinical data, use initial validation to filter out the dataset
is_usable = validate_and_save_cohort_info(
    is_final=False,  # Initial validation for filtering out datasets lacking required data
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False
)