p3/preprocess/Large_B-cell_Lymphoma/code/GSE173263.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE173263"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE173263"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE173263.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE173263.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE173263.csv"
json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/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
# Based on background info, this is a GEP (Gene Expression Profile) study
is_gene_available = True

# 2. Data Availability and Type Conversion
# 2.1 Data Availability
# Trait (response to R-CHOP) is in row 2 
trait_row = 2
# Age not available
age_row = None  
# Gender not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1].strip()
    if "early failure" in value:
        return 1
    elif "remission" in value:
        return 0
    return None

def convert_age(value):
    return None

def convert_gender(value):
    return None

# 3. Save 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 extracted features
    preview = preview_df(clinical_features)
    
    # Save clinical features
    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 format (e.g., '11715100_at', '11715101_s_at'), these appear to be Affymetrix probe IDs 
# rather than standard human gene symbols. They need to be mapped to HGNC gene symbols.

requires_gene_mapping = True
# Report discovery of missing gene annotation
print("Gene 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"
)