output/preprocess/Allergies/code/GSE203409.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE203409"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE203409"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE203409.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE203409.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE203409.csv"
json_path = "./output/z1/preprocess/Allergies/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  # Gene expression profiling of keratinocytes is suitable

# 2. Variable Availability and Data Type Conversion

# Given the dataset is an in vitro keratinocyte cell line experiment, there is no human trait/age/gender.
trait_row = None
age_row = None
gender_row = None

def _extract_value(x):
    if x is None:
        return None
    if isinstance(x, str):
        parts = x.split(":", 1)
        return parts[1].strip() if len(parts) == 2 else x.strip()
    return x

def convert_trait(x):
    # Heuristic mapper for allergy-related datasets (not used here since trait_row is None)
    val = _extract_value(x)
    if val is None:
        return None
    s = val.lower()
    # Map obvious control/healthy to 0
    if any(k in s for k in ["control", "untreated", "healthy", "shc"]):
        return 0
    # Map allergen exposure or allergic status to 1
    if any(k in s for k in ["allerg", "derp", "mite", "sensitized", "atopic"]):
        return 1
    # Cytokines/mediators not clearly allergy phenotype; set None
    return None

def convert_age(x):
    # Extract numeric age if present
    val = _extract_value(x)
    if val is None:
        return None
    nums = re.findall(r"[+-]?\d+(?:\.\d+)?", str(val))
    if not nums:
        return None
    try:
        age = float(nums[0])
    except Exception:
        return None
    # Filter unrealistic ages
    if age < 0 or age > 120:
        return None
    return age

def convert_gender(x):
    val = _extract_value(x)
    if val is None:
        return None
    s = str(val).strip().lower()
    if s in ["female", "f", "woman", "women"]:
        return 0
    if s in ["male", "m", "man", "men"]:
        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 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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    clinical_preview = preview_df(selected_clinical_df)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# 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
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# Decide on the columns: probe IDs are in 'ID' and gene symbols are in 'Symbol'
id_col = 'ID'
gene_symbol_col = 'Symbol'

# 2. Get the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)

# 3. Convert probe-level data to gene-level expression using the mapping
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save gene-level data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Determine if clinical data (trait) is available from earlier steps
is_trait_available = (('trait_row' in locals()) and (trait_row is not None))

if is_trait_available:
    # 2. Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
    # 3. Handle missing values
    linked_data = handle_missing_values(linked_data, trait)
    # 4. Bias checks
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
    # 5. 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 available and linked."
    )
    # 6. Save linked data if usable
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        unbiased_linked_data.to_csv(out_data_file)
else:
    # No clinical trait/age/gender in this in vitro dataset; skip linking and downstream steps
    _ = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=False,  # placeholder; trait not available
        df=normalized_gene_data,
        note="INFO: In vitro keratinocyte cell line; no human trait/age/gender available."
    )