p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE87847.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE87847"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE87847"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE87847.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE87847.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE87847.csv"
json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability 
is_gene_available = True  # RNA data from blood and tissue, suitable for gene expression analysis

# 2.1 Data Availability
trait_row = 0  # ASD vs typically developing 
gender_row = 2  # Sex information available
age_row = None  # Age not provided in sample characteristics

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert ASD status to binary"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower().strip()
    if 'autism' in value:
        return 1
    elif 'typically developing' in value:
        return 0
    return None

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

def convert_age(value: str) -> float:
    """Placeholder function since age data not available"""
    return None

# 3. Save metadata
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
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
)

# Preview the processed clinical data
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 some data preview to verify structure
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

print("\nData preview:")
preview_subset = genetic_data.iloc[:5, :5]
print(preview_subset)
# Most identifiers appear to be standard gene symbols (e.g. A1BG, A2M, AAAS)
# A few exceptions like 7A5 but overall these look like human gene symbols
# No mapping needed since data is already in HGNC gene symbol format
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
genetic_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)

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

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
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="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."
)

# 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)