p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE111175.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE111175"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE111175"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE111175.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.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
# Based on series title and summary, this dataset contains leukocyte gene expression data
is_gene_available = True

# 2.1 Data Availability

# For trait: diagnosis field contains ASD vs TD info
trait_row = 3

# For age: age in months is available
age_row = 2

# For gender: gender is recorded but constant (all male)
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert diagnosis to binary: ASD=1, TD=0, others=None"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[1] if ': ' in x else x
    if value == 'ASD':
        return 1
    elif value == 'TD': 
        return 0
    return None

def convert_age(x):
    """Convert age string to float (in months)"""
    if not isinstance(x, str):
        return None
    try:
        value = x.split(': ')[1] if ': ' in x else x
        return float(value)
    except:
        return None

# gender_row is None so no need for convert_gender

# 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
selected_clinical = geo_select_clinical_features(clinical_data, 
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait,
                                               age_row=age_row, 
                                               convert_age=convert_age)

# Preview the extracted features
preview = preview_df(selected_clinical)
print("Preview of extracted clinical features:")
print(preview)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.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 are ILMN (Illumina) probe IDs from a microarray platform, not gene symbols
# They need to be mapped to official gene symbols for analysis
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 gene identifiers in gene expression data match the 'ID' column, 
# and gene symbols are in the 'Symbol' column of gene_metadata
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping dataframe from gene annotation
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Print preview
print("Preview of mapped gene expression data:")
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(selected_clinical, 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 age."
)

# 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)