p1/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/preprocess/1/Allergies/GSE203409.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE203409.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE203409.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 series title and summary ("Gene expression profiling..."),
# we conclude that gene expression data is indeed available.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics dictionary, we see:
#   0 -> cell line info
#   1 -> knockdown info
#   2 -> treatment info
#   3 -> treatment compound concentration
# This dataset is an in vitro study using a HaCaT cell line.
# There is no human-level "Allergies" status, no age, and no gender data.
# Hence, for each variable (trait, age, gender), data is NOT available.

trait_row = None
age_row = None
gender_row = None

# Even though data is not available, we must define conversion functions.
# If called, they would handle extraction and conversion logic. Here, they return None.

def convert_trait(value: str):
    # Placeholder implementation.
    # Usually, we'd parse 'value' after the colon, e.g. value.split(':')[-1].strip().
    # But since data is not available, always return None.
    return None

def convert_age(value: str):
    # Placeholder implementation.
    return None

def convert_gender(value: str):
    # Placeholder implementation.
    return None

# 3. Save Metadata
# We do an initial validation using 'validate_and_save_cohort_info'.
# Trait data availability is determined by (trait_row is not None).
is_trait_available = (trait_row is not None)

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
# Since trait_row is None, we skip the clinical extraction step.
# (No substep needed as there is no clinical data to extract.)
# 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])
# Based on inspection, the identifiers "ILMN_xxxxxx" appear to be Illumina probe IDs, not standard human gene symbols.
# Therefore, gene symbol mapping is required.
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) From the preview, the "ID" column in 'gene_annotation' matches the probe IDs in 'gene_data' (both have "ILMN_xxxxx" format),
#    and the "Symbol" column holds the gene symbol information.
# 2) Create a mapping dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# For confirmation, print out the shape and a small preview of the mapped gene_data.
print("Gene data shape after mapping:", gene_data.shape)
print(gene_data.head())
import pandas as pd

# STEP 5: 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}")

# Since in earlier steps trait_row was None, we have no clinical data to link.
# Hence, there's no trait column to process. We'll skip linking and further steps
# that require the trait. However, we must still perform a final validation.

# Prepare a dummy DataFrame for the final validation
dummy_df = pd.DataFrame()

# We must provide is_biased and df to the final validation.
# Because trait data is not available, this dataset won't be usable.
is_biased = False  # Arbitrarily set; since trait is unavailable, "is_usable" will be False anyway.

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,      # Gene data is available
    is_trait_available=False,    # Trait data is not available
    is_biased=is_biased,
    df=dummy_df,
    note="No trait data available; skipping linking."
)

# 6. If data were usable, we would save it; otherwise we do nothing
if is_usable:
    print("Data is unexpectedly marked usable, but trait is unavailable. Skipping save.")