p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE65106.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE65106"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE65106"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE65106.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE65106.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE65106.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)
# Gene Expression Data Availability
# The data contains gene expression profile of fibroblasts, iPSCs, and neurons
is_gene_available = True

# Variable Availability and Data Type Conversion
# Trait availability - can be determined from 'disease type' field
trait_row = 1

def convert_trait(value: str) -> int:
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip()
    if value == 'ASD':
        return 1
    elif value in ['Normal', 'WT']:
        return 0
    return None

# Age availability - available in 'donor age' field
age_row = 3

def convert_age(value: str) -> float:
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip()
    if value == 'embryonic':
        return None
    try:
        return float(value)
    except:
        return None

# Gender availability - data shows all subjects are male, so constant value
gender_row = None

def convert_gender(value: str) -> int:
    return None

# Save metadata for initial filtering
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))

# Extract clinical features since trait_row is not None
clinical_df = geo_select_clinical_features(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
preview = preview_df(clinical_df)
print("Preview of processed 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 appear to be probe IDs from a microarray platform (numeric identifiers)
# rather than standard human gene symbols. They 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)
# Get gene mapping from annotation data
# 'ID' column contains probe identifiers matching gene expression data
# 'gene_assignment' column contains gene symbols (need to be extracted)
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') 

# Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the processed gene data
print("\nPreview of gene expression data:")
print(gene_data.iloc[:5, :5])
# 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_data, 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)
# 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
# Convert the first row values to be the column names
linked_data.columns = linked_data.iloc[0]
linked_data = linked_data.iloc[1:]
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 skin fibroblasts, iPSCs and neurons. Contains ASD and healthy controls. Age data available for most samples."
)

# 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)