p3/preprocess/Large_B-cell_Lymphoma/code/GSE114022.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE114022"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE114022"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE114022.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE114022.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE114022.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
# The title and design indicate this is gene expression data from cell lines
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability

# Treatment (YK-S vs YK-R) can be used as binary trait 
trait_row = 1

# Cell lines only, no patient age data
age_row = None  

# Cell lines only, no gender data
gender_row = None

# 2.2 Data Type Conversion
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1]
    # YK-S vs YK-R comparison (exclude DMSO control)
    if value == "yk-s":
        return 0
    elif value == "yk-r": 
        return 1
    return None

def convert_age(value):
    # Not available
    return None

def convert_gender(value):
    # Not available 
    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. Extract Clinical Features
if trait_row is not None:
    clinical_features = geo_select_clinical_features(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 results
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical data
    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())
# The gene identifiers start with "ILMN_" which indicates these are Illumina probe IDs
# They need to be mapped to standard human gene symbols for analysis
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"
)