output/preprocess/Arrhythmia/code/GSE115574.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE115574"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE115574"

# Output paths
out_data_file = "./output/z1/preprocess/Arrhythmia/GSE115574.csv"
out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE115574.csv"
json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"


# Step 1: Initial Data Loading
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 2: Dataset Analysis and Clinical Feature Extraction
import os
import re

# 1) Gene expression data availability
is_gene_available = True  # Affymetrix human gene expression microarrays (mRNA), not miRNA-only or methylation-only.

# 2) Variable availability and converters

# Trait (Arrhythmia) is represented by disease state: AFib vs sinus rhythm at row 0.
trait_row = 0

# Age and gender are not provided in the sample characteristics shown.
age_row = None
gender_row = None

def _extract_value(x):
    if x is None:
        return None
    # Handle potential NaNs
    try:
        import pandas as pd
        if pd.isna(x):
            return None
    except Exception:
        pass
    # Extract substring after the first colon if present
    s = str(x)
    parts = s.split(":", 1)
    val = parts[1] if len(parts) == 2 else parts[0]
    return val.strip().lower()

def convert_trait(x):
    """
    Map Arrhythmia status: AF/AFib -> 1; Sinus rhythm (SR) -> 0; otherwise None.
    """
    val = _extract_value(x)
    if val is None:
        return None

    # Positive for arrhythmia if 'atrial fibrillation' or 'afib' or ' af ' as a word
    if ("atrial fibrillation" in val) or re.search(r"\bafib\b", val) or re.search(r"\baf\b", val):
        return 1
    # Negative if 'sinus rhythm' or ' sr ' as a word
    if ("sinus rhythm" in val) or re.search(r"\bsr\b", val):
        return 0

    return None

# Define but not used since rows are unavailable
def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3) Save metadata (initial filtering)
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
)

# 4) Clinical feature extraction (only if clinical data is available)
if trait_row is not None:
    selected_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=None,
        gender_row=gender_row,
        convert_gender=None
    )
    # Preview and save
    preview = preview_df(selected_clinical_df)
    print(preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# Step 3: Gene Data Extraction
# 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])

# Step 4: Gene Identifier Review
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 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 6: Gene Identifier Mapping
# Identify the appropriate columns for probe IDs and gene symbols based on the annotation preview
probe_col = 'ID'
gene_symbol_col = 'Gene Symbol'

# Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Optionally save the gene-level data for later steps
import os
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Check bias and remove biased demographic features
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
note = "INFO: Trait from disease state (AFib=1 vs SR=0); Age and Gender unavailable in sample characteristics."
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=is_trait_biased,
    df=unbiased_linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)