p3/preprocess/X-Linked_Lymphoproliferative_Syndrome/code/GSE248835.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE248835"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE248835"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE248835.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE248835.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE248835.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
# From the background info, this is a gene expression study analyzing B-cell lymphoma tumor characteristics
is_gene_available = True  

# 2.1 Data Availability 
# From sample characteristics:
# Row 1 shows treatment arm, can be used to infer trait(disease state): "Axicabtagene Ciloleucel" vs "Standard of Care Chemotherapy"
# Age and gender are not available
trait_row = 1
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> float:
    """
    Convert treatment arm information to binary values.
    1: Axicabtagene Ciloleucel (experimental treatment)
    0: Standard of Care Chemotherapy (control)
    """
    if pd.isna(value):
        return None
    if isinstance(value, str):
        if "treatment arm:" in value:
            if "Axicabtagene Ciloleucel" in value:
                return 1
            elif "Standard of Care Chemotherapy" in value:
                return 0
    return None

convert_age = None
convert_gender = 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:
    selected_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
    )
    
    print("Preview of extracted clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save clinical data
    selected_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
# Review gene identifiers
# Based on the first few gene IDs (1, 2, 3, etc) and series info showing this is an array study,
# these are probe IDs that need to be mapped to gene symbols

requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Display gene annotation structure
print("Gene annotation columns:")
print(gene_annotation.columns)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))
# Get gene mapping - "ID" is the probe identifier column matching the gene expression data
# "Gene_Signature_Name" seems to contain gene names/signatures
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col='ID', 
    gene_col='Gene_Signature_Name'
)

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

# Preview the resulting gene expression data
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows and columns of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_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, gene_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 from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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)