p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE42133.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE42133"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE42133"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE42133.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE42133.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.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
is_gene_available = True  # Based on background info showing leukocyte gene expression data

# 2.1 Data Availability 
trait_row = 0  # Diagnosis info in row 0
gender_row = None  # Gender is constant (all male) so considered unavailable
age_row = None  # No age data available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert diagnosis to binary: Control=0, ASD=1"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip().upper()
    if value == 'CONTROL':
        return 0
    elif value == 'ASD':
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to float (unused as age not available)"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (unused as gender not available)"""
    return None

# 3. Save initial 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 since trait data is available
clinical_features = 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 features
print("Preview of clinical features:")
print(preview_df(clinical_features))

# Save clinical data
clinical_features.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)
# The identifiers starting with "ILMN_" are Illumina probe IDs, not human gene symbols
# These need to be mapped to gene symbols for proper analysis
requires_gene_mapping = True
# Try different prefix combinations to extract platform annotation
prefixes_to_try = [
    ['!Platform_table_begin'],  # Standard platform table marker
    ['^PLATFORM'],             # Platform section marker
    ['!Platform_']             # Platform metadata
]

gene_metadata = None
for prefixes in prefixes_to_try:
    data, _ = filter_content_by_prefix(
        source=soft_file_path,
        prefixes_a=prefixes,
        unselect=False,
        source_type='file',
        return_df_a=True
    )
    # Check if we got meaningful data (non-empty and has some non-NaN symbols)
    if not data.empty and 'Symbol' in data.columns and not data['Symbol'].isna().all():
        gene_metadata = data.iloc[1:] if prefixes[0].endswith('begin') else data
        break

# Preview column names and first few values
preview = preview_df(gene_metadata if gene_metadata is not None else pd.DataFrame())
print("\nGene annotation columns and sample values:")
print(preview)
# 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)
# Create gene mapping from probe IDs to gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

# Convert probe expression to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview result
print("\nConverted gene expression data preview:")
preview = preview_df(gene_data)
print(preview)
# 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_features, 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)