output/preprocess/Epilepsy/code/GSE74571.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE74571"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE74571"

# Output paths
out_data_file = "./output/z3/preprocess/Epilepsy/GSE74571.csv"
out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE74571.csv"
out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE74571.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
import re

# 1) Gene Expression Data Availability
is_gene_available = True  # Based on series summary indicating gene expression profiling on GSCs

# 2) Variable Availability and Data Type Conversion

# From the provided Sample Characteristics Dictionary:
# {0: ['cell/tissue type: ...'], 1: ['culture type: ...']}
# There is no explicit or inferable Epilepsy status, age, or gender information.

trait_row = None
age_row = None
gender_row = None

# Conversion helpers
def _after_colon(value: str) -> str:
    if value is None:
        return ''
    parts = str(value).split(':', 1)
    return parts[1].strip().lower() if len(parts) > 1 else str(value).strip().lower()

def convert_trait(value):
    # Binary: 1 = Epilepsy case, 0 = non-epilepsy control
    v = _after_colon(value)
    if not v:
        return None
    # Positive epilepsy indicators
    if any(term in v for term in ['epilepsy', 'epileptic', 'seizure', 'tle', 'temporal lobe epilepsy']):
        return 1
    # Clear control indicators
    if any(term in v for term in ['control', 'healthy', 'non-epileptic', 'non epileptic', 'no epilepsy']):
        return 0
    # Ambiguous disease terms (e.g., GBM) are not epilepsy; avoid forcing to 0 without context
    return None

def convert_age(value):
    # Continuous: extract numeric age in years if present
    v = _after_colon(value)
    if not v:
        return None
    m = re.search(r'(\d+(\.\d+)?)', v)
    if m:
        try:
            return float(m.group(1))
        except Exception:
            return None
    return None

def convert_gender(value):
    # Binary: female -> 0, male -> 1
    v = _after_colon(value)
    if not v:
        return None
    if v in ['female', 'f', 'woman', 'girl', 'wmn']:
        return 0
    if v in ['male', 'm', 'man', 'boy']:
        return 1
    return None

# 3) Save Metadata (initial filtering)
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) Clinical Feature Extraction (skip because trait_row is None)
# If in future data becomes available:
# if trait_row is not None:
#     selected = 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 = preview_df(selected, n=5)
#     selected.to_csv(out_clinical_data_file, index=True)