p3/preprocess/Werner_Syndrome/code/GSE48761.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Werner_Syndrome"
cohort = "GSE48761"

# Input paths
in_trait_dir = "../DATA/GEO/Werner_Syndrome"
in_cohort_dir = "../DATA/GEO/Werner_Syndrome/GSE48761"

# Output paths
out_data_file = "./output/preprocess/3/Werner_Syndrome/GSE48761.csv"
out_gene_data_file = "./output/preprocess/3/Werner_Syndrome/gene_data/GSE48761.csv"
out_clinical_data_file = "./output/preprocess/3/Werner_Syndrome/clinical_data/GSE48761.csv"
json_path = "./output/preprocess/3/Werner_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
# Based on background info, this dataset contains gene expression data from fibroblasts and iPSCs
is_gene_available = True

# 2.1 Data Availability
# Row 2 contains genotype info (WT vs WRN mutant) which indicates Werner Syndrome status
trait_row = 2
# Row 1 contains age information
age_row = 1  
# Row 0 contains gender information
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert genotype to binary trait value (0=control, 1=Werner Syndrome)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if 'WRN mutant' in value:
        return 1
    elif 'WT' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age string to float value in years"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'embryonic':
        return 0.0  # Assign 0 for embryonic samples
    try:
        return float(value)
    except:
        return None

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

# 3. Save Initial Metadata
# Perform initial validation
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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of extracted clinical features:")
    print(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_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
# Based on the gene IDs shown (e.g. 7892501), these appear to be probe IDs from a microarray platform
# rather than standard human gene symbols. They will 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)

# Preview annotation structure 
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. From checking both dataframes, 'ID' in annotation matches probe IDs in expression data
# and 'gene_assignment' contains gene symbols
prob_col = 'ID'
gene_col = 'gene_assignment'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Normalize gene symbols to standard format
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview results
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of gene expression 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)