p3/preprocess/Stomach_Cancer/code/GSE147163.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stomach_Cancer"
cohort = "GSE147163"

# Input paths
in_trait_dir = "../DATA/GEO/Stomach_Cancer"
in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE147163"

# Output paths
out_data_file = "./output/preprocess/3/Stomach_Cancer/GSE147163.csv"
out_gene_data_file = "./output/preprocess/3/Stomach_Cancer/gene_data/GSE147163.csv"
out_clinical_data_file = "./output/preprocess/3/Stomach_Cancer/clinical_data/GSE147163.csv"
json_path = "./output/preprocess/3/Stomach_Cancer/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
# Based on background info mentioning gene expression profile via BeadChip array
is_gene_available = True

# 2.1 Data Availability
# From sample chars, see only 'tissue: gastric cancer' which indicates tumor samples
# Age and gender info not available
trait_row = 0  # The tissue info can indicate tumor status
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert tissue value to binary trait status.
    gastric cancer = 1 (tumor)"""
    if pd.isna(value):
        return None
    value = value.split(': ')[1].lower()
    if 'gastric cancer' in value:
        return 1
    return None
    
def convert_age(value: str) -> float:
    """Convert age value to float.
    Not used since age data unavailable."""
    return None

def convert_gender(value: str) -> int:
    """Convert gender value to binary.
    Not used since gender data unavailable."""
    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=True  # trait_row is not None
)

# 4. Clinical Feature Extraction
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
)

print("Preview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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])
# The IDs are ILMN_ prefixed Illumina probe identifiers, not human gene symbols
# They will need to be mapped to standard gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Get mapping between probe IDs and gene symbols from annotation data
# 'ID' column has probe IDs and 'Symbol' column has gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Map probe-level data to genes
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Print summary info
print("Shape of original probe data:", genetic_data.shape)
print("Shape after mapping to genes:", gene_data.shape)
print("\nFirst few mapped gene symbols:")
print(list(gene_data.index)[:10])
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)