p3/preprocess/Eczema/code/GSE63741.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE63741"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE63741"

# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE63741.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE63741.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE63741.csv"
json_path = "./output/preprocess/3/Eczema/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 series title and summary mentioning gene expression analyses, and design mentioning total RNA
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Looking at sample characteristics:
# The trait (eczema) status can't be determined from any single row, but needs to be inferred from the descriptions
# 'Contact Eczema (KE)' vs others mentioned in the background
trait_row = 1  
# No age info
age_row = None
# No gender info  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert disease type to binary trait indicator
    1: Contact Eczema (KE), 0: other conditions/healthy"""
    if not isinstance(value, str):
        return None
    value = value.lower()
    if "contact eczema" in value or "ke" in value:
        return 1
    elif any(x in value for x in ["psoriasis", "atopic dermatitis", "lichen planus", "healthy", "control"]):
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Not used as age data not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Not used as gender data not available"""
    return None

# 3. Save Metadata
# Initial filtering - only checking data availability
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. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
if trait_row is not None:
    clinical_features = 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 extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.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 IDs shown in the gene expression data are probe IDs from a microarray platform
# They need to be mapped to standard human gene symbols for downstream analysis
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. Looking at the data:
# - Gene expression data uses numeric IDs in the 'ID' column
# - Gene annotation data has corresponding IDs in the 'ID' column 
# - The 'description' field contains gene symbols at the start before colon

# 2. Extract gene mapping
# Extract gene symbol from description field (text before colon)
gene_metadata['Gene'] = gene_metadata['description'].str.extract(r'^([^:]+):', expand=False)
# Some cleanup of gene symbols: remove parentheses content and whitespace
gene_metadata['Gene'] = gene_metadata['Gene'].str.replace(r'\s*\([^)]*\)', '', regex=True).str.strip()
# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene')

# 3. Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Print shape and preview
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(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_data = pd.read_csv(out_clinical_data_file, index_col=0)

# Fix trait values based on background information before linking
sample_order = list(gene_data.columns)  # Get sample order from gene data
sample_groups = {
    'Contact Eczema': range(0, 30),     # First 30 samples
    'Control': range(30, 150)           # Remaining samples
} 

# Create new trait series with corrected values
trait_values = pd.Series(0, index=sample_order)  # Initialize all as controls
trait_values.iloc[sample_groups['Contact Eczema']] = 1  # Set contact eczema cases
clinical_data.iloc[0] = trait_values  # Replace original trait row

linked_data = geo_link_clinical_genetic_data(clinical_data, 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 about inflammatory skin diseases including contact eczema, atopic dermatitis, lichen planus, psoriasis and healthy controls."
)

# 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)