p3/preprocess/Cystic_Fibrosis/code/GSE76347.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE76347"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE76347"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE76347.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE76347.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE76347.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/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
# The dataset contains nasal epithelial cell gene expression data from microarray analysis 
is_gene_available = True

# 2.1 Data Availability
# trait_row: Everyone has CF (key 0), so trait data is constant and not useful for association study
trait_row = None  
# age_row: Age not recorded in characteristics 
age_row = None
# gender_row: Gender not recorded in characteristics
gender_row = None

# 2.2 Data Type Conversion Functions (though not used since data unavailable)
def convert_trait(x):
    if pd.isna(x):
        return None
    val = str(x).split(":")[-1].strip().upper()
    if "CF" in val:
        return 1
    return None

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

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

# 3. Save Metadata
# is_trait_available is False since trait_row is None (constant trait value)
is_trait_available = False if trait_row is None else True
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. Skip clinical feature extraction since trait_row is None
# 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])
# These are not human gene symbols but probe IDs from the Illumina array platform
# They need to be mapped to official human gene symbols for consistent 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))
# Extract probe ID and gene symbol mapping data
prob_col = 'ID'  # Column containing probe identifiers matching genetic_df index

# Function to extract gene symbol from gene_assignment string
def extract_gene_symbol(text):
    if pd.isna(text) or text == '---':
        return None
    # Split by // and take the second element which contains the gene symbol
    parts = text.split('//')
    if len(parts) >= 2:
        return parts[1].strip()
    return None

# Create mapping dataframe with proper gene symbols
mapping_df = gene_metadata[['ID']].copy()
mapping_df['Gene'] = gene_metadata['gene_assignment'].apply(extract_gene_symbol)
mapping_df = mapping_df.dropna()

# Apply mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview the mapped gene expression data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(gene_data.index[:10])
print("\nPreview of expression values:")
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. Final validation - dataset not usable due to constant trait
empty_df = pd.DataFrame()  # Empty dataframe since no linked data possible
is_biased = True  # Explicitly mark as biased since trait is constant
validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No usable trait data
    is_biased=is_biased,
    df=empty_df,
    note="All subjects have CF (constant trait). Gene expression data saved but not suitable for trait association analysis."
)