p3/preprocess/Large_B-cell_Lymphoma/code/GSE243973.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE243973"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE243973"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE243973.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE243973.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE243973.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
# Yes - Series summary mentions transcriptomic profiling
is_gene_available = True

# 2.1 Feature Key Identification
# Trait - Row 0 contains disease state info
trait_row = 0
# Age - Not available in characteristics 
age_row = None  
# Gender - Not available in characteristics
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> int:
    """Convert disease status to binary: 1 for LBCL, 0 for control"""
    if pd.isna(x):
        return None
    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if 'large b-cell lymphoma' in value:
        return 1
    elif 'healthy control' in value:
        return 0
    return None

def convert_age(x: str) -> float:
    """Not used but defined for completeness"""
    return None

def convert_gender(x: str) -> int:
    """Not used but defined for completeness"""
    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. Extract Clinical Features
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 the data
    preview = preview_df(clinical_features)
    print("Clinical features preview:", preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# These appear to be standard human gene symbols (HGNC format)
# e.g. ABCF1, ACACA, ADAR are well-known human gene symbols
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)

# 3. Handle missing values
linked_data = handle_missing_values(df=linked_data, trait_col=trait)

# 4. Check for biases and remove biased demographic features 
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate dataset quality and save metadata
note = ""
if is_biased:
    note = "The trait distribution is severely biased."

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=is_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)