output/preprocess/Allergies/code/GSE84046.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE84046"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE84046"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE84046.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE84046.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE84046.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
# 1) Gene expression data availability
is_gene_available = True  # Whole-genome gene expression in adipose tissue is described in the background.

# 2) Variable availability and conversion functions
# Trait: Allergies -> Not available in this dataset
trait_row = None

# Age: Not explicitly available; only date of birth provided without sampling date -> cannot compute precise age
age_row = None

# Gender: Available under key 4 ("sexe: Male/Female")
gender_row = 4

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

def convert_trait(x):
    # No allergy-related information provided; return None
    return None

def convert_age(x):
    # Date of birth provided but no sampling date to compute age accurately
    return None

def convert_gender(x):
    val = _extract_after_colon(x)
    if val is None:
        return None
    v = val.strip().lower()
    if v in ["female", "f", "woman", "women"]:
        return 0
    if v 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 becomes available in future revisions, uncomment the following:
# 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 = preview_df(selected_clinical_df, n=5)
#     selected_clinical_df.to_csv(out_clinical_data_file)