# Estimating AutoAntibody Signatures to Detect Autoimmune Disease Patient Subsets

## TL;DR

This paper introduces a Bayesian hierarchical model to improve autoantibody signature detection from gel electrophoresis data, enabling better autoantibody subset identification in autoimmune disease research.

## Contribution

The study develops a novel Bayesian approach for pattern alignment and dewarping in gel electrophoresis autoradiography, enhancing autoantibody signature discovery and disease subgroup detection.

## Key findings

- Improved clustering of autoantibody profiles after pre-processing
- Enhanced accuracy in autoantibody subset detection
- Validated method on scleroderma patient data

## Abstract

Autoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [$\textbf{G}$el $\textbf{E}$lectrophoresis and band detection on $\textbf{A}$utoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors - including irrelevant intensity differences and warping of gels - have challenged automation of pattern discovery from autoradiograms. In this paper, we address these limitations using a Bayesian hierarchical model with shrinkage priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. We show the pre-processing creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering. Finally, we demonstrate the utility of the proposed methods with GEA data from scleroderma patients.