p1/preprocess/Epilepsy/code/GSE65106.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE65106"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE65106"

# Output paths
out_data_file = "./output/preprocess/1/Epilepsy/GSE65106.csv"
out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE65106.csv"
out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE65106.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 title and summary, this dataset appears to be a gene expression study.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# The trait is "Epilepsy", but the sample characteristics only mention "ASD", "Normal", or "WT".
# No mention of "Epilepsy" is found, so trait_row is None.
trait_row = None

# For age, key 3 has multiple donor ages. Hence it is available.
age_row = 3

# For gender, key 4 has both "Male" and "Female", so it is available.
gender_row = 4

# 2.2 Data Type Conversion
# Define conversion functions for trait, age, and gender.
def convert_trait(value: str):
    # Trait not available in this dataset, so we return None.
    return None

def convert_age(value: str):
    # Extract the substring after the colon and strip whitespace
    val = value.split(":")[-1].strip()
    # Convert numeric ages to float, mark non-numeric (e.g., 'embryonic') as None
    return float(val) if val.isdigit() else None

def convert_gender(value: str):
    # Extract the substring after the colon and convert to binary (female=0, male=1)
    val = value.split(":")[-1].strip().lower()
    if val == "male":
        return 1
    elif val == "female":
        return 0
    return None

# 3. Save Metadata (initial filtering)
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
# This step is only performed if trait_row is not None. Here, trait_row is None, so we skip.
# 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 the numeric format of these identifiers, they do not match standard human gene symbols.
# Therefore, we conclude that these IDs represent array probe identifiers and require mapping to gene symbols.
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))
# STEP6: Gene Identifier Mapping

# 1. Based on the annotation preview and the reviewer's suggestion that "mrna_assignment" may contain 
#    more appropriate human gene symbols, we will use 'mrna_assignment' for the gene symbol column.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='mrna_assignment')

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

# For inspection, let's print the shape of the resulting gene_data and its first few rows.
print("Mapped gene_data shape:", gene_data.shape)
print(gene_data.head())
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
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    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
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 5) Final validation (trait is available)
    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 in Step 2."
    )

    # 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
    # Perform final validation indicating that we lack trait data
    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,  # Arbitrary non-None to skip usage
        df=empty_df,
        note="No trait data was found; linking and final dataset output are skipped."
    )