p3/preprocess/Stomach_Cancer/code/GSE118916.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stomach_Cancer"
cohort = "GSE118916"

# Input paths
in_trait_dir = "../DATA/GEO/Stomach_Cancer"
in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE118916"

# Output paths
out_data_file = "./output/preprocess/3/Stomach_Cancer/GSE118916.csv"
out_gene_data_file = "./output/preprocess/3/Stomach_Cancer/gene_data/GSE118916.csv"
out_clinical_data_file = "./output/preprocess/3/Stomach_Cancer/clinical_data/GSE118916.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
# From background info, this is a microarray gene expression study on GAC tissues
is_gene_available = True

# 2.1 Data Availability
# Trait (cancer vs normal) not explicitly given in sample characteristics
trait_row = None  # No explicit trait data in characteristics
age_row = None  # No age data available
gender_row = 0  # Gender data in row 0

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Not needed since trait data not available
    return None

def convert_age(value):
    # Not needed since age data not available  
    return None

def convert_gender(value):
    if pd.isna(value):
        return None
    val = value.split(": ")[1].strip().lower()
    if val == "female":
        return 0
    elif val == "male":
        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. Clinical Feature Extraction
# Skip since trait_row is None
# 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 identifiers look like Affymetrix probe IDs (e.g. '11715100_at', '11715101_s_at')
# rather than standard human gene symbols. These will need to be mapped to gene symbols.
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 gene mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Convert probe measurements to gene expression using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize gene symbols to ensure consistency
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene data to file 
gene_data.to_csv(out_gene_data_file)

# Print some info about the mapped data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])
# 1. Normalize gene symbols in gene expression 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)

# Create minimal dataframe since no clinical data available
linked_data = gene_data.T  # Transpose to match expected format 
is_biased = True  # Dataset without trait data is unusable for association studies

# Validate and save metadata
note = "Successfully normalized probe-level to gene-level expression using NCBI Gene database. However, no clinical trait data available."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=linked_data,
    note=note
)