p3/preprocess/Metabolic_Rate/code/GSE40873.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Metabolic_Rate"
cohort = "GSE40873"

# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE40873"

# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE40873.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE40873.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE40873.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
# Yes - The background indicates this is a genome-wide gene expression analysis
is_gene_available = True

# 2. Variable availability and data type conversion
# 2.1 Row identifiers
trait_row = 2  # Metabolic rate can be inferred from survival time 
age_row = None  # Age not available
gender_row = None  # Gender not available

# 2.2 Conversion functions
def convert_trait(x):
    """Convert multicentric occurrence-free survival days to metabolic rate"""
    try:
        # Extract number after colon and convert to float
        days = float(x.split(': ')[1])
        # Higher survival time indicates better metabolic function 
        # Normalize to 0-1 range using 2500 days as max
        return min(days/2500, 1.0)
    except:
        return None

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

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

# 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 
clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait
)

# Preview and save clinical data
print(preview_df(clinical_df))
clinical_df.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])
# Based on ID format (e.g., '1007_s_at'), these are Affymetrix probe IDs, not gene symbols
# They need to be mapped to standard gene symbols for 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))
# Get gene mapping from annotation data
# 'ID' column stores probe IDs matching gene expression data
# 'Gene Symbol' column stores target gene symbols
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

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

# Preview mapped gene expression data 
print("Shape after gene mapping:", gene_data.shape)
print("\nFirst few mapped genes and their expression values:")
print(gene_data.head())

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