p3/preprocess/Celiac_Disease/code/GSE138297.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE138297"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE138297"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE138297.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE138297.csv"
json_path = "./output/preprocess/3/Celiac_Disease/cohort_info.json"

# Get paths to the 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 feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on the background info, microarray analysis was performed, so gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 6  # experimental condition shows disease/control
age_row = 3    # age is available in years
gender_row = 1 # sex is available with binary encoding

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (0 for control, 1 for case)"""
    if not value or ':' not in value:
        return None
    value = value.split(': ')[1].strip()
    if 'Allogenic' in value:
        return 1  # case group receiving donor FMT
    elif 'Autologous' in value:
        return 0  # control group receiving own FMT
    return None

def convert_age(value: str) -> float:
    """Convert age value to continuous number"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (0 for female, 1 for male)"""
    if not value or ':' not in value:
        return None
    try:
        # Note: In data female=1, male=0, but we need to flip it to match our convention
        return 1 - int(value.split(': ')[1])  # Flip 1->0 for female, 0->1 for male
    except:
        return 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
# Since trait_row is not None, we proceed with clinical data extraction
selected_clinical = 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 clinical data
print("Preview of clinical data:")
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# The row identifiers appear to be microarray probe IDs (16650001, etc.)
# These are not standard human gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# Extract gene mapping information between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

# Apply mapping to convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview output
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save
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)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# 4. Check for biased features  
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data from duodenal biopsies of children with celiac disease and controls"
)

# 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)