p3/preprocess/Duchenne_Muscular_Dystrophy/code/GSE48828.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"
cohort = "GSE48828"

# Input paths
in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE48828"

# Output paths
out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE48828.csv"
out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE48828.csv"
out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE48828.csv"
json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on background info, this is an Affymetrix exon array study measuring gene expression
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identifiers for each variable
trait_row = 0  # 'diagnosis' row contains trait info
age_row = 2    # 'age (yrs)' row contains age info 
gender_row = 1 # 'gender' row contains gender info

# 2.2 Conversion functions
def convert_trait(value: str) -> Optional[int]:
    """Convert trait status to binary"""
    if not value or ':' not in value:
        return None
    diagnosis = value.split(': ')[1].strip().lower()
    if 'duchenne muscular dystrophy' in diagnosis:
        return 1
    elif 'normal' in diagnosis:
        return 0
    return None

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

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary"""
    if not value or ':' not in value:
        return None
    gender = value.split(': ')[1].strip().lower()
    if gender == 'f':
        return 0
    elif gender == 'm':
        return 1
    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
if trait_row is not None:
    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 the processed clinical data
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# The row IDs are numerical probe IDs from microarray platforms, not human gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify columns for gene identifiers and symbols
# 'ID' column contains same identifiers as gene expression data
# 'gene_assignment' contains gene symbols but needs parsing

# Function to parse gene symbols from complex strings
def parse_gene_symbols(text):
    if text == '---' or pd.isna(text):
        return None
    # Split by /// to handle multiple assignments
    gene_entries = text.split('///')
    symbols = []
    for entry in gene_entries:
        parts = entry.strip().split('//')
        if len(parts) >= 3:  # We need at least 3 parts to get to the gene symbol
            symbol = parts[1].strip()  # Gene symbol is in the second position
            if symbol != '---':
                symbols.append(symbol)
    return symbols if symbols else None

# Create initial mapping dataframe
mapping_df = gene_metadata[['ID', 'gene_assignment']].copy()

# Extract gene symbols and clean up mapping
mapping_df['Gene'] = mapping_df['gene_assignment'].apply(parse_gene_symbols)
mapping_df = mapping_df[['ID', 'Gene']].dropna(subset=['Gene'])

# Explode lists of genes into separate rows
mapping_df = mapping_df.explode('Gene')

# Apply gene mapping to probe-level measurements
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Normalize gene symbols to standard form
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print shape and preview mapped data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Since gene_data is empty, we need to remap gene symbols
def parse_gene_symbols(text):
    if text == '---' or pd.isna(text):
        return None
    # Split entries by /// for multiple assignments
    entries = text.split('///')
    symbols = []
    for entry in entries:
        parts = [p.strip() for p in entry.split('//')]
        if len(parts) >= 2:  # Need at least 2 parts
            symbol = parts[1]  # Second part contains the gene symbol
            if symbol != '---':
                symbols.append(symbol)
    return symbols if symbols else None

# Create initial mapping dataframe
mapping_df = gene_metadata[['ID', 'gene_assignment']].copy()

# Extract gene symbols and clean up mapping
mapping_df['Gene'] = mapping_df['gene_assignment'].apply(parse_gene_symbols)
mapping_df = mapping_df[['ID', 'Gene']].dropna(subset=['Gene'])

# Explode lists of genes into separate rows
mapping_df = mapping_df.explode('Gene')
print(f"Number of probe-gene mappings: {len(mapping_df)}")

# Apply gene mapping to probe-level measurements
gene_data = apply_gene_mapping(genetic_df, mapping_df)
print(f"Number of genes after mapping: {len(gene_data)}")

# After remapping, proceed with the rest of step 7
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
print(f"Number of genes after normalization: {len(gene_data)}")
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)

# 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)