output/preprocess/Epilepsy/code/GSE273630.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE273630"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE273630"

# Output paths
out_data_file = "./output/z3/preprocess/Epilepsy/GSE273630.csv"
out_gene_data_file = "./output/z3/preprocess/Epilepsy/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/z3/preprocess/Epilepsy/clinical_data/GSE273630.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 gene expression availability
is_gene_available = True  # NanoString digital transcript panel indicates gene expression data

# Step 2: Variable availability and conversion functions

# Availability from the provided Sample Characteristics Dictionary and background info:
# - Trait (Epilepsy): Excluded by design; no sample-level field -> not available
# - Age: No per-sample age field provided -> not available
# - Gender: All participants are males by design (constant) -> not available
trait_row = None
age_row = None
gender_row = None

# Conversion functions (defined for interface completeness; they won't be used since rows are None)
def _extract_value(cell):
    if cell is None:
        return None
    if isinstance(cell, str):
        parts = cell.split(":", 1)
        return parts[1].strip() if len(parts) == 2 else cell.strip()
    return cell

def convert_trait(x):
    # Map epilepsy-related information to binary (0/1); default to None if unknown
    val = _extract_value(x)
    if val is None:
        return None
    s = str(val).strip().lower()
    # Positive indications
    positive_kw = ["epilep", "seizure", "ictal", "sz"]
    # Negative phrases
    negative_kw = ["no epilepsy", "non-epilep", "without epilepsy", "seizure-free", "no seizure", "none"]
    if any(k in s for k in negative_kw):
        return 0
    if any(k in s for k in positive_kw):
        # avoid false positives if explicitly negated
        if "no " in s or "not " in s:
            return 0
        return 1
    return None

def convert_age(x):
    val = _extract_value(x)
    if val is None:
        return None
    s = str(val)
    # Extract first number (integer or float)
    import re
    m = re.search(r"(-?\d+\.?\d*)", s)
    if m:
        try:
            return float(m.group(1))
        except:
            return None
    return None

def convert_gender(x):
    val = _extract_value(x)
    if val is None:
        return None
    s = str(val).strip().lower()
    if s in ["male", "m", "man", "boy"]:
        return 1
    if s in ["female", "f", "woman", "girl"]:
        return 0
    # Try to infer from single letters embedded
    if "male" in s:
        return 1
    if "female" in s:
        return 0
    return None

# Step 3: Save metadata using 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)

# Step 4: Clinical feature extraction
# Skipped because trait_row is None (no clinical trait data available for Epilepsy in this dataset)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
requires_gene_mapping = False
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Data Normalization and Linking
# 1. Normalize the obtained gene data and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-6. Proceed only if clinical data with trait exists (guard safely via locals().get)
if (locals().get('selected_clinical_data') is not None) and (locals().get('trait_row') is not None):
    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # Bias check and remove biased demographic features
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Final validation and save cohort info
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=True,
        is_biased=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Clinical features extracted and linked successfully."
    )

    # Save linked data only if usable
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)
else:
    # No clinical trait data available; only gene data saved in this step
    print("No clinical trait data available; skipping linking and final validation for this cohort.")