p1/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/preprocess/1/Allergies/GSE84046.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE84046.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE84046.csv"
json_path = "./output/preprocess/1/Allergies/cohort_info.json"

# STEP1
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)
# 1. Gene Expression Data Availability
is_gene_available = True  # The study clearly mentions "whole genome gene expression" in adipose tissue.

# 2. Variable Availability
# Trait (Allergies): Not found in the sample characteristics.
trait_row = None
# Age: We can infer from date of birth, which is stored under key=5 "date of birth".
age_row = 5
# Gender: Found under key=4 "sexe: Male/Female".
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(value: str):
    # No trait data, so return None.
    return None

def convert_age(value: str):
    # Example format: "date of birth (dd-mm-yyyy): 1952-06-17"
    # We parse out '1952-06-17' and convert it to approximate age.
    try:
        date_str = value.split(':', 1)[1].strip()  # e.g. "1952-06-17"
        birth_year = int(date_str.split('-')[0])
        # Approximate age by subtracting from current year
        approx_age = 2023 - birth_year
        if approx_age < 0 or approx_age > 120:
            return None
        return float(approx_age)
    except:
        return None

def convert_gender(value: str):
    # Example format: "sexe: Male" or "sexe: Female"
    try:
        gender_str = value.split(':', 1)[1].strip().lower()
        if gender_str == "male":
            return 1
        elif gender_str == "female":
            return 0
        else:
            return None
    except:
        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
# Since trait_row is None, we skip extracting clinical features for the trait "Allergies".
# STEP3
# 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])
# The gene identifiers in the provided index are numeric, suggesting they are not standard human gene symbols.
# These likely need to be mapped to gene symbols.

requires_gene_mapping = True
# STEP5
# 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: Gene Identifier Mapping

# 1. Decide which columns in 'gene_annotation' match the probe IDs in 'gene_data' (i.e., "ID") 
#    and which column contains text that can lead to actual gene symbols (i.e., "gene_assignment").
prob_col = "ID"
gene_col = "gene_assignment"

# 2. Get a gene mapping dataframe using these two columns.
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply the gene mapping to convert probe-level data to gene-level data.
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
# STEP 7: Data Normalization and Linking

# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)

# 2. Since trait data is missing, skip linking clinical and genetic data, 
#    skip missing-value handling and bias detection for the trait.

# 3. Conduct final validation and record info.
#    Since trait data is unavailable, set is_trait_available=False, 
#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
dummy_df = pd.DataFrame()
is_usable = 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,
    df=dummy_df,
    note="No trait data found; skipped clinical-linking steps."
)

# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
if is_usable:
    dummy_df.to_csv(out_data_file, index=True)