p1/preprocess/Allergies/code/GSE182740.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE182740"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE182740"

# Output paths
out_data_file = "./output/preprocess/1/Allergies/GSE182740.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE182740.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE182740.csv"
json_path = "./output/preprocess/1/Allergies/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
# Based on the background information ("Global mRNA expression" is mentioned),
# we conclude that gene expression data is available:
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# After reviewing the sample characteristics dictionary, we see that
# key=1 contains "disease: Psoriasis", "disease: Atopic_dermatitis", "disease: Mixed", "disease: Normal_skin".
# We can use this to infer a binary trait for "Allergies" if "Atopic_dermatitis" or "Mixed" is present, else 0.
trait_row = 1  # because it provides disease info that we can map to 'Allergies'

# No mention of age or gender in the dictionary, so these are not available:
age_row = None
gender_row = None

# Define the conversion functions.
def convert_trait(value: str):
    """
    Convert a string like "disease: Psoriasis" to a binary indicator for the trait "Allergies".
    We parse the substring after "disease:" and map:
    - "Atopic_dermatitis" or "Mixed" -> 1 (indicative of 'Allergies')
    - Otherwise -> 0
    Unknown or unexpected -> None
    """
    if not isinstance(value, str):
        return None

    # Typically "disease: something", split by colon
    parts = value.split(":", 1)
    if len(parts) < 2:
        return None
    disease_str = parts[1].strip().lower()  # e.g. "psoriasis", "atopic_dermatitis", "mixed", "normal_skin"

    if "atopic_dermatitis" in disease_str or "mixed" in disease_str:
        return 1
    elif "psoriasis" in disease_str or "normal_skin" in disease_str:
        return 0
    else:
        return None

def convert_age(value: str):
    """
    Data not available; placeholder function returning None.
    """
    return None

def convert_gender(value: str):
    """
    Data not available; placeholder function returning None.
    """
    return None

# 3. Save Metadata (initial filtering)
# Trait data is available if trait_row != None
is_trait_available = (trait_row is not None)

# Perform the initial validation and save metadata.
# The function returns True if the dataset passes final validation,
# but here we only do the initial filtering (is_final=False).
is_usable = 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
# Proceed only if trait_row is not None
if trait_row is not None:
    # Assuming "clinical_data" is the previously obtained clinical DataFrame
    clinical_data_selected = 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 the selected clinical data
    preview_result = preview_df(clinical_data_selected)
    print("Clinical data preview:", preview_result)

    # Save the extracted clinical features
    clinical_data_selected.to_csv(out_clinical_data_file, index=False)
# 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 given identifiers (e.g., '1007_s_at', '1053_at') appear to be Affymetrix probe IDs, not official gene symbols.
# Hence, we need to map them to recognized gene symbols.
print("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 keys in the gene annotation store the probe IDs and gene symbols
#    From our observation, 'ID' matches the probe IDs (e.g., '1007_s_at'), 
#    and 'Gene Symbol' stores the gene symbols.

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3. Convert probe-level measurements to gene-level measurements 
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (At this stage, 'gene_data' now holds gene-level expression data.)
import pandas as pd

# STEP 7: Data Normalization and Linking

# 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)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data_selected, normalized_gene_data)

# 3. Handle missing values
cleaned_data = handle_missing_values(linked_data, trait)

# 4. Determine bias in trait and demographic features
trait_biased, final_data = judge_and_remove_biased_features(cleaned_data, trait)

# 5. Final validation and save metadata
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=trait_biased,
    df=final_data,
    note="Processed with standard GEO pipeline."
)

# 6. If data is usable, save the final linked data
if is_usable:
    final_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Data not usable; skipping final output.")