  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.
