p1/preprocess/Atherosclerosis/code/GSE123086.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE123086"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123086"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE123086.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE123086.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE123086.csv"
json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"

# STEP 1: Initial Data Loading

# 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,
    prefixes_a=background_prefixes,
    prefixes_b=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. Determine gene expression data availability
is_gene_available = True  # Based on the microarray gene expression description

# 2. Identify rows and define conversion functions
trait_row = 1    # Row containing primary diagnoses info including ATHEROSCLEROSIS
age_row = 3      # Row containing various "age: ..." entries
gender_row = 2   # Row containing "Sex: Male" or "Sex: Female" entries

def convert_trait(value: str):
    """Convert to binary: 1 if contains 'ATHEROSCLEROSIS', else 0."""
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().upper()
    return 1 if 'ATHEROSCLEROSIS' in val else 0

def convert_age(value: str):
    """Convert to continuous age in years. Unknown or non-numeric => None."""
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str):
    """Convert to binary: Female => 0, Male => 1, else None."""
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().upper()
    if val == 'MALE':
        return 1
    elif val == 'FEMALE':
        return 0
    return None

# 3. Initial filtering and metadata save
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 and preview if trait data is available
if trait_row is not None:
    extracted_clinical_df = 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 = preview_df(extracted_clinical_df)
    print("Preview of extracted clinical features:", preview)
    extracted_clinical_df.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])
# Based on the numeric format, these are not typical human gene symbols and likely require mapping.
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))
# Gene Identifier Mapping
prob_col = "ID"       # The column in gene_annotation that matches the gene_data index
gene_col = "ENTREZ_GENE_ID"  # The column in gene_annotation to treat as the gene symbol for mapping

# 1. Get the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# 3. Preview mapped gene_data
print("Preview of gene_data after mapping:")
print(preview_df(gene_data))
import os
import pandas as pd

# STEP 7

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
if not os.path.exists(out_clinical_data_file):
    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
    dummy_df = pd.DataFrame()
    trait_biased = True  # Mark as biased or unusable because we lack any trait information
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=trait_biased,
        df=dummy_df,
        note="No trait data found. This dataset is not usable for final analysis."
    )
    print("Clinical data file not found. Skipping linking and final data export.")
else:
    # 2. Read the clinical data without using index_col; assign the correct row index manually.
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
    # We have exactly three rows: trait, Age, Gender
    selected_clinical_df.index = [trait, "Age", "Gender"]
    
    # Link the clinical data with genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
    trait_biased, df = judge_and_remove_biased_features(df, trait)

    # 5. Perform final validation with full dataset information.
    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=df,
        note="Final step with linking, missing-value handling, bias checks."
    )

    # 6. If the data is usable, save the final linked data.
    if is_usable:
        df.to_csv(out_data_file)
        print(f"Final linked data saved to: {out_data_file}")
    else:
        print("Dataset is not usable or is severely biased. No final data saved.")