p1/preprocess/Epilepsy/code/GSE273630.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE273630"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE273630"

# Output paths
out_data_file = "./output/preprocess/1/Epilepsy/GSE273630.csv"
out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE273630.csv"
json_path = "./output/preprocess/1/Epilepsy/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)
# Step 1: Gene Expression Data Availability
# Based on the background info referencing a digital transcript panel (Nanostring) for inflammatory genes,
# we conclude that it is likely gene expression data.
is_gene_available = True

# Step 2: Variable Availability and Data Type Conversion
# From the sample characteristics dictionary, we only have {0: ['tissue: Peripheral blood cells']}.
# No usable fields for trait, age, or gender are present, or they are constant for all samples.
# Hence all rows are None.

trait_row = None
age_row = None
gender_row = None

# Define the conversion functions (though no real data is available to be converted).
def convert_trait(value: str):
    return None

def convert_age(value: str):
    return None

def convert_gender(value: str):
    return None

# Step 3: Save Metadata by initial filtering
# Trait availability is determined by whether trait_row is None.
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
)

# Step 4: Clinical Feature Extraction
# Since trait_row is None, clinical data for the trait is not available.
# Therefore, according to the instruction, we skip this substep.
# 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])
# These gene identifiers appear to be standard human gene symbols.
print("requires_gene_mapping = False")
import os
import pandas as pd

# STEP7

# 1) Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
if os.path.exists(out_clinical_data_file):
    # 2) Link the clinical and gene expression data
    # The CSV was saved with index=False in Step 2, so we reload it as a single-row DataFrame and assign the row index.
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
    selected_clinical_df.index = [trait]

    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

    # 3) Handle missing values
    final_data = handle_missing_values(linked_data, trait_col=trait)

    # 4) Evaluate bias in the trait (and remove biased demographics if any)
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 5) Final validation
    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="Trait data successfully extracted; row index fixed at Step 7."
    )

    # 6) If the dataset is usable, save
    if is_usable:
        final_data.to_csv(out_data_file)
else:
    # If the clinical file does not exist, the trait is unavailable
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,
        df=empty_df,
        note="No trait data was found; linking and final dataset output are skipped."
    )