p1/preprocess/Epilepsy/code/GSE123993.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE123993"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE123993"

# Output paths
out_data_file = "./output/preprocess/1/Epilepsy/GSE123993.csv"
out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE123993.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)
# 1. Gene Expression Data Availability
#    Based on the background mentioning "Affymetrix HuGene 2.1ST arrays" for whole genome gene expression,
#    we conclude that gene expression data is available for this dataset.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
#    There is no entry for the Epilepsy trait in the sample characteristics.
#    Hence, trait_row is None.
trait_row = None

#    Age information is not explicitly available in the sample characteristics (only "aged above 65").
#    There's no numeric or variable key that specifies different ages per sample.
#    Hence, age_row is None.
age_row = None

#    For gender, row 1 has two distinct values: "Sex: Male" and "Sex: Female". This is suitable for analysis.
#    Hence, gender_row is 1.
gender_row = 1

# 2.2 Data Type Conversion
#    For trait and age, there's no data. We'll only define the convert function for gender.

def convert_gender(val: str):
    """
    Convert gender data into a binary format:
    - Male -> 1
    - Female -> 0
    """
    # First split by colon if present
    parts = val.split(':')
    if len(parts) > 1:
        val = parts[-1].strip()
    val_lower = val.lower()
    if 'male' in val_lower:
        return 1
    elif 'female' in val_lower:
        return 0
    return None

# 3. Save Metadata
#    Perform the initial filtering step. The trait is not available (trait_row is None).
#    So is_trait_available is False. We'll store these metadata.

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 this step because trait data isn't available.
# 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 provided identifiers like "16650001" appear to be numeric probe IDs rather than standard human gene symbols.
# Hence, gene mapping is needed.

print("requires_gene_mapping = True")
# STEP5
import pandas as pd
import io

# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
annotation_text, _ = filter_content_by_prefix(
    source=soft_file,
    prefixes_a=['^', '!', '#'],
    unselect=True,
    source_type='file',
    return_df_a=False,
    return_df_b=False
)

# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
gene_annotation = pd.read_csv(
    io.StringIO(annotation_text),
    delimiter='\t',
    on_bad_lines='skip',
    engine='python'
)

print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1 & 2. Determine matching columns: use "ID" as the probe identifier and "gene_assignment" for gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")

# 3. Convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)
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
    # Reload the clinical data. It has 3 rows (trait, Age, Gender) and columns for samples.
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
    # Force the row index to match [trait, 'Age', 'Gender'] so geo_link_clinical_genetic_data works correctly.
    selected_clinical_df.index = [trait, "Age", "Gender"]

    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 manually set in 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."
    )