p3/preprocess/Liver_Cancer/code/GSE45032.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE45032"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE45032"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE45032.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE45032.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE45032.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"

# Get file paths for 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 clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Series title and summary indicate gene expression by microarray

# 2.1 Data Availability
trait_row = 0  # HCC vs CHC in cell type field
age_row = 3    # Age data available with multiple values
gender_row = 2 # Gender data available with both male and female

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert cell type to binary: 1 for HCC, 0 for CHC"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1].lower() if ": " in value else value.lower()
    if "hepatocallular carcinoma" in value or "hcc" in value:
        return 1
    elif "chronic hepatitis" in value or "chc" in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age string to float"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1] if ": " in value else value
    try:
        return float(value.replace("yrs", "").strip())
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1].lower() if ": " in value else value.lower()
    if "female" in value:
        return 0
    elif "male" in value:
        return 1
    return None

# 3. Save Initial 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
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_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)

# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])

# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Based on the index showing numeric IDs (1,2,3...), these are not human gene symbols 
# and will need to be mapped to proper gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)

# Preview the annotation data structure
print("Gene Annotation Preview:")
preview = preview_df(gene_annotation)
print(json.dumps(preview, indent=2))

print("\nGene Annotation Analysis:")
print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")

# Update validation info to show dataset cannot be used due to missing gene mapping
validate_and_save_cohort_info(
    is_final=False,  
    cohort=cohort,
    info_path=json_path,
    is_gene_available=False,  # Set to False since gene expression data is not mappable
    is_trait_available=trait_row is not None,
    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
)