p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE285666.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE285666"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE285666"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE285666.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE285666.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE285666.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
# Yes, this contains genome-wide exon-level array data for gene expression
is_gene_available = True

# 2. Variable Availability and Row Numbers
# Disease state row contains the trait info (WS vs control)
trait_row = 0  

# Age and gender data not available in sample characteristics
age_row = None
gender_row = None

# Convert Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert trait value to binary: 1 for patient, 0 for control"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'patient' in value:
        return 1
    elif 'control' in value:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Dummy function since age data not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Dummy function since gender data not available"""
    return None

# 3. Save Initial 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. Extract Clinical Features
if trait_row is not None:
    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 clinical data:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    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)
# These identifiers appear to be probe IDs from a microarray experiment rather than human gene symbols
# They are numeric IDs that need to be mapped to proper gene symbols for biological interpretation
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. The 'ID' column in gene annotation matches the probe IDs in expression data
# The gene symbols can be extracted from 'gene_assignment' using extract_human_gene_symbols

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

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

# Preview the mapped gene data
print("\nPreview of gene expression data after mapping:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene 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)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_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)