p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE123302.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE123302"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE123302"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE123302.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE123302.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE123302.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 available (Affymetrix Human Gene 2.0 array data)
is_gene_available = True

# 2.1 Data availability
# Trait (diagnosis) is in row 0, gender in row 1, age not available
trait_row = 0  
gender_row = 1
age_row = None

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert ASD/Non-TD/TD diagnosis to binary (ASD=1, TD/Non-TD=0)"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1].strip()
    return 1 if value == "ASD" else 0

def convert_gender(value: str) -> int:
    """Convert gender to binary (female=0, male=1)"""
    if pd.isna(value):
        return None
    value = value.split(": ")[1].strip().lower()
    return 1 if value == "male" else 0

# Age conversion function not needed since age data unavailable
convert_age = None

# 3. Save initial metadata 
is_usable = 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_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
)

# Preview the extracted clinical data
preview = preview_df(clinical_df)
print("Preview of clinical data:")
print(preview)

# 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 numeric identifiers appear to be probe IDs from a microarray platform
# They are not standard human gene symbols and will need to be mapped
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)
# First check what other columns are available in the gene metadata
print("All columns in gene annotation data:")
print(gene_metadata.columns)

# Get gene mapping dataframe using ID and GB_ACC columns
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GB_ACC')

# Convert probe-level measurements to gene expression data using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the mapped gene expression data 
print("\nPreview of mapped gene expression data:")
print(gene_data.head().to_string())

# Additional checks
print("\nNumber of genes:", len(gene_data))
print("Number of samples:", len(gene_data.columns))

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# First review gene annotation data again to check for gene symbol columns
print("Gene annotation data first 5 rows:")
print(gene_metadata.head().to_string())

# Looking at SPOT_ID format which contains chromosome locations
# We can get more gene annotations using these locations
print("\nMapping from chromosome locations to gene symbols...")
mapping_df = gene_metadata[['ID', 'SPOT_ID']].copy()
mapping_df['Gene'] = mapping_df['SPOT_ID'].str.extract(r'chr\d+:(\d+-\d+)')
mapping_df = mapping_df[['ID', 'Gene']].dropna()

# Apply mapping to get gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data with gene symbols
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

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

# 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 microarray data with mapped gene symbols. Sample size adequate. Clinical data includes ASD diagnosis and gender."
)

# 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)