output/preprocess/Allergies/code/GSE205151.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE205151"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE205151"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE205151.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE205151.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE205151.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
import pandas as pd

# 1) Gene expression data availability (Nanostring mRNA profiling => gene data available)
is_gene_available = True

# 2) Variable availability and conversion functions
# Based on the provided Sample Characteristics, none of the required variables are present.
trait_row = None
age_row = None
gender_row = None

def _after_colon(x):
    if x is None:
        return None
    if isinstance(x, str):
        parts = x.split(":", 1)
        v = parts[1] if len(parts) > 1 else parts[0]
        return v.strip()
    return x

def convert_trait(x):
    # Binary: 1 = has allergies/atopy; 0 = no allergies
    v = _after_colon(x)
    if v is None:
        return None
    s = str(v).strip().lower()
    if s in {"na", "n/a", "unknown", "not available", "none", ""}:
        return None
    # positive terms
    pos_terms = {"yes", "y", "1", "true", "positive", "pos", "allergic", "atopic", "atopy", "sensitized", "sensitised"}
    neg_terms = {"no", "n", "0", "false", "negative", "neg", "non-allergic", "nonallergic", "non-atopic", "nonatopic",
                 "unsensitized", "not sensitized", "none"}
    if s in pos_terms:
        return 1
    if s in neg_terms:
        return 0
    # Heuristics for free text
    if any(k in s for k in ["allerg", "atopy", "atopic", "sensitiz"]):
        # Assume presence if phrased affirmatively without negations
        if any(neg in s for neg in ["no ", "non-", "non ", "none", "without", "negative"]):
            return 0
        return 1
    return None

def convert_age(x):
    # Continuous: extract first float number (years)
    v = _after_colon(x)
    if v is None:
        return None
    s = str(v).strip().lower()
    if s in {"na", "n/a", "unknown", "not available", ""}:
        return None
    m = re.search(r"[-+]?\d*\.\d+|[-+]?\d+", s)
    if m:
        try:
            return float(m.group())
        except Exception:
            return None
    return None

def convert_gender(x):
    # Binary: female=0, male=1
    v = _after_colon(x)
    if v is None:
        return None
    s = str(v).strip().lower()
    if s in {"na", "n/a", "unknown", "not available", ""}:
        return None
    if s in {"f", "female", "woman", "girl"}:
        return 0
    if s in {"m", "male", "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 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
    )
    # Preview and save
    print(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)