output/preprocess/Epilepsy/code/GSE123993.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE123993"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE123993"

# Output paths
out_data_file = "./output/z3/preprocess/Epilepsy/GSE123993.csv"
out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE123993.csv"
json_path = "./output/z3/preprocess/Epilepsy/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# Step 1: Determine data availability
is_gene_available = True  # Affymetrix HuGene 2.1 ST arrays indicate gene expression data
# Trait of interest is Epilepsy; this dataset is a vitamin D supplementation study with no epilepsy phenotype recorded
trait_row = None

# No explicit age field in the sample characteristics; background says all >65 (constant, not useful)
age_row = None

# Gender is available under 'Sex'
gender_row = 1

# Step 2: Define converters
import re
import pandas as pd

def _after_colon(val: str) -> str:
    if val is None:
        return ""
    s = str(val)
    parts = s.split(":", 1)
    return parts[1].strip() if len(parts) == 2 else s.strip()

def convert_trait(x):
    # Binary mapping for Epilepsy trait if present in other datasets; not used here (trait_row is None)
    v = _after_colon(x).lower()
    if v in {"epilepsy", "epileptic", "case", "patient", "seizure", "seizures"}:
        return 1
    if v in {"control", "healthy", "normal", "non-epilepsy", "none", "na"}:
        return 0
    return None

def convert_age(x):
    # Continuous mapping; extract numeric age in years if present
    v = _after_colon(x)
    if not v:
        return None
    # Find a number (integer or float)
    m = re.search(r"(\d+(?:\.\d+)?)", v)
    if not m:
        return None
    try:
        return float(m.group(1))
    except Exception:
        return None

def convert_gender(x):
    # Binary mapping: female->0, male->1
    v = _after_colon(x).lower()
    if v in {"male", "m"}:
        return 1
    if v in {"female", "f"}:
        return 0
    return None

# Step 3: Initial filtering and save 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
)

# Step 4: Clinical feature extraction (skip if trait not available)
if trait_row is not None:
    selected_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 if age_row is not None else None,
        gender_row=gender_row,
        convert_gender=convert_gender if gender_row is not None else None
    )
    clinical_preview = preview_df(selected_clinical_df, n=5)
    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)