p3/preprocess/Gaucher_Disease/code/GSE124283.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Gaucher_Disease"
cohort = "GSE124283"

# Input paths
in_trait_dir = "../DATA/GEO/Gaucher_Disease"
in_cohort_dir = "../DATA/GEO/Gaucher_Disease/GSE124283"

# Output paths
out_data_file = "./output/preprocess/3/Gaucher_Disease/GSE124283.csv"
out_gene_data_file = "./output/preprocess/3/Gaucher_Disease/gene_data/GSE124283.csv"
out_clinical_data_file = "./output/preprocess/3/Gaucher_Disease/clinical_data/GSE124283.csv"
json_path = "./output/preprocess/3/Gaucher_Disease/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data
# Since this is a microarray study of gene expression changes in skin fibroblasts,
# gene expression data should be available
is_gene_available = True

# 2. Variable Availability and Conversion Functions
# 2.1 Data rows
trait_row = 2  # 'condition' field contains disease status
gender_row = 3  # 'gender' field is available
age_row = None # Age information not available

# 2.2 Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait values to binary (0: control, 1: Gaucher)"""
    if not value or "N/A" in value:
        return None
    value = value.split(": ")[1] if ": " in value else value
    if "Control" in value:
        return 0
    elif "Gaucher" in value:
        return 1
    return None  # Other conditions like NPC are not relevant

def convert_gender(value: str) -> int:
    """Convert gender values to binary (0: female, 1: male)"""
    if not value or "N/A" in value:
        return None
    value = value.split(": ")[1] if ": " in value else value
    if value == "K":  # K likely means "kobieta" (female in Polish)
        return 0
    elif value == "M":  # M likely means male
        return 1
    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 feature extraction
selected_clinical_df = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview the selected features
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))

# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These gene IDs look like human gene symbols (e.g., A1BG, A2M, AAAS, AACS etc.)
# Therefore no mapping is required
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical data file 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Gene expression data from fibroblasts of Gaucher disease patients and healthy controls. Also contains samples from NPC disease patients which were excluded from analysis."
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
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")