p3/preprocess/Glioblastoma/code/GSE148949.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glioblastoma"
cohort = "GSE148949"

# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE148949"

# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE148949.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE148949.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE148949.csv"
json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # This is a gene expression array dataset per Series_overall_design

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability
trait_row = None  # Not available as trait status in characteristics
age_row = None  # No age info
gender_row = None  # No gender info 

# 2.2 Data Type Conversion
# Since none of the clinical variables are available, we don't need conversion functions
convert_trait = None
convert_age = None  
convert_gender = None

# 3. Save Metadata
# Initial filtering based on 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
# Skip since trait_row is None
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# These identifiers appear to be probe IDs (numerical) rather than human gene symbols
# The first identifier "1/2-SBSRNA4" may be a gene symbol but most others are numeric IDs "41334", "41335" etc.
# For GEO expression data from microarray platforms, probe IDs typically need mapping to gene symbols
requires_gene_mapping = True
# Inspect the raw SOFT file to understand its structure
import gzip
print("Preview of SOFT file content:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for i, line in enumerate(f):
        if i < 20:  # Print first 20 lines
            print(line.strip())
        else:
            break

# Extract sections with platform annotation
platform_info = filter_content_by_prefix(soft_file,
                                       prefixes_a=['!Platform_'],  
                                       source_type='file',
                                       return_df_a=False)[0]

print("\nPlatform annotation preview:")
platform_lines = platform_info.split('\n')[:20]  # First 20 lines
for line in platform_lines:
    print(line)
# Extract gene annotation from SOFT file
gene_metadata = get_gene_annotation(soft_file)

# Print column names to identify relevant columns
print("Annotation columns:", gene_metadata.columns.tolist())

# Preview first few lines to verify data format 
print("\nAnnotation preview:")
print(gene_metadata.head())

# Extract probe ID and gene symbol columns for mapping
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF')

# Map probe IDs to gene symbols and aggregate expression values
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Normalize gene symbols to standard nomenclature
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print shape and preview to verify mapping results
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# Save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Create empty clinical features since no trait data available
clinical_features = pd.DataFrame(index=[trait])

# Link clinical and genetic data (will be just genetic data with empty clinical features)
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Check for biases
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate and save cohort info
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="Dataset contains gene expression data but no glioblastoma trait information"
)

# Save linked data if usable (will not save since trait data is missing)
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)