p3/preprocess/Huntingtons_Disease/code/GSE95843.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Huntingtons_Disease"
cohort = "GSE95843"

# Input paths
in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE95843"

# Output paths
out_data_file = "./output/preprocess/3/Huntingtons_Disease/GSE95843.csv"
out_gene_data_file = "./output/preprocess/3/Huntingtons_Disease/gene_data/GSE95843.csv"
out_clinical_data_file = "./output/preprocess/3/Huntingtons_Disease/clinical_data/GSE95843.csv"
json_path = "./output/preprocess/3/Huntingtons_Disease/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(soft_file_path)  # Changed to use soft_file_path

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on background info (embryoid bodies/stem cells study), likely contains gene expression data

# 2.1 Data Availability 
trait_row = 294  # "treatment: plasma from a Huntington's disease mouse model"
age_row = None  # Age not available 
gender_row = None  # Gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert HD treatment info to binary"""
    if not value:
        return None
    if ":" not in value:
        return None
    value = value.split(":")[1].strip().lower()
    if "huntington" in value:
        return 1
    return 0

def convert_age(value: str) -> float:
    return None  # Not used

def convert_gender(value: str) -> int:
    return None  # Not used

# 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 selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The row IDs 'A1BG', 'A2M', 'AAAS' etc. are standard human gene symbols
# No mapping required as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# Get clinical features with revised trait conversion
def convert_trait(value: str) -> int:
    """Convert HD treatment info to binary"""
    if not isinstance(value, str):
        return None
    if "treatment:" not in value:
        return None
    value = value.split("treatment:")[1].strip().lower()
    return 1 if "huntington" in value else 0

clinical_features = geo_select_clinical_features(
    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
)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from mouse embryoid bodies treated with plasma from Huntington's disease model mice"
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
)

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