p3/preprocess/X-Linked_Lymphoproliferative_Syndrome/code/GSE239832.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE239832"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE239832"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE239832.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE239832.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE239832.csv"
json_path = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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
# Yes - Based on the background info, this is a gene expression profiling study
is_gene_available = True

# 2. Clinical Feature Analysis
# Gender data is in row 0, with clear female/male values 
# No age data
# Trait data can be inferred from cll-fish data in row 1, where 17p indicates our target condition
gender_row = 0
age_row = None
trait_row = 1

def convert_gender(value: str) -> int:
    """Convert gender data to binary format"""
    if not value or ':' not in value:
        return None
    gender = value.split(':')[1].strip().lower()
    if gender == 'f':
        return 0
    elif gender == 'm':
        return 1
    return None

def convert_age(value: str) -> float:
    """Placeholder for age conversion"""
    return None

def convert_trait(value: str) -> int:
    """Convert 17p deletion status (trait) to binary"""
    if not value or ':' not in value:
        return None
    cll_fish = value.split(':')[1].strip().lower()
    # 1 if 17p deletion is present, 0 otherwise
    return 1 if '17p' in cll_fish else 0

# 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
clinical_df = 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 and save clinical data
print("Preview of processed clinical data:")
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if i < 10:  # Print first 10 lines
            print(line.strip())
        elif "!series_matrix_table_begin" in line:
            print("\nFound table marker at line", i)
            # Print next 3 lines after marker
            for _ in range(3):
                print(next(f).strip())
            break
# Observe first few gene identifiers - they appear to be Affymetrix probe IDs (e.g. 1007_s_at)
# rather than standard human gene symbols. We need to map these to gene symbols.
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure 
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Extract mapping between probe IDs (ID) and gene symbols (Gene Symbol)
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview gene data shape and first few rows
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst 5 rows of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols in gene expression data
genetic_data = normalize_gene_symbols_in_index(gene_data)

# Check if normalization resulted in empty data
if genetic_data.shape[0] == 0:
    print("\nNo valid genes remained after normalization.")
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=True,
        is_biased=True,
        df=selected_clinical_df,
        note="Gene symbol normalization resulted in empty dataset"
    )
else:
    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
    genetic_data.to_csv(out_gene_data_file)
    print("\nGene data shape (normalized gene-level):", genetic_data.shape)

    # 2. Link clinical and genetic data
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

    # 4. Check for bias in features
    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save dataset metadata
    note = "Dataset contains gene expression data but normalization removed all genes."
    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_trait_biased,
        df=linked_data,
        note=note
    )

    # 6. Save linked data if usable
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)