p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE89594.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autism_spectrum_disorder_(ASD)"
cohort = "GSE89594"

# Input paths
in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE89594"

# Output paths
out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE89594.csv"
out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE89594.csv"
out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.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 dataset contains gene expression data from blood samples
is_gene_available = True

# 2.1 Data Availability
# Trait (ASD) data is in row 0 under "diagnosis"
trait_row = 0

# Age data is in row 2
age_row = 2

# Gender data is in row 3  
gender_row = 3

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.lower().split(": ")[-1]
    if "autism" in x or "asd" in x:
        return 1
    elif "control" in x:
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        # Extract number before 'y'
        age = int(x.split(": ")[-1].replace('y',''))
        return age
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    x = x.lower().split(": ")[-1]
    if x == "female":
        return 0
    elif x == "male":
        return 1
    return None

# 3. Save 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. Clinical Feature Extraction
clinical_features = 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 extracted features
preview_dict = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_dict)

# Save clinical features
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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)
# These identifiers are numerical indices (1, 2, 3, etc.) and not valid human gene symbols
# We need to map them to actual gene symbols for the data to be biologically meaningful
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)
# Looking at gene identifiers in both expression data and annotation, 'ID' column matches
# Gene symbols are in 'GENE_SYMBOL' column
mapping = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# Apply gene mapping to convert probe data to gene expression data 
gene_data = apply_gene_mapping(genetic_data, mapping)

# Preview result
print("Preview of mapped gene expression data:")
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_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, age 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)