A Horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging

TL;DR

This paper introduces a novel Bayesian mixture model using a Horseshoe prior for multi-tier classification of brain regions in light sheet microscopy, enabling more nuanced detection of activation levels beyond binary classification.

Contribution

It proposes a new discrete mixture of continuous scale mixtures prior, called cluster-shrinkage Horseshoe, for improved Bayesian sparse estimation and multi-tier classification in brain imaging.

Findings

More biologically meaningful and interpretable results.

Effective discrimination between active and inactive regions.

Ability to rank discoveries into tiers of importance.

Abstract

In this paper, we focus on identifying differentially activated brain regions using a light sheet fluorescence microscopy - a recently developed technique for whole-brain imaging. Most existing statistical methods solve this problem by partitioning the brain regions into two classes: significantly and non-significantly activated. However, for the brain imaging problem at the center of our study, such binary grouping may provide overly simplistic discoveries by filtering out weak but important signals, that are typically adulterated by the noise present in the data. To overcome this limitation, we introduce a new Bayesian approach that allows classifying the brain regions into several tiers with varying degrees of relevance. Our approach is based on a combination of shrinkage priors - widely used in regression and multiple hypothesis testing problems - and mixture models - commonly used in model-based clustering. In contrast to the existing regularizing prior distributions, which use either the spike-and-slab prior or continuous scale mixtures, our class of priors is based on a discrete mixture of continuous scale mixtures and devises a cluster-shrinkage version of the Horseshoe prior. As a result, our approach provides a more general setting for Bayesian sparse estimation, drastically reduces the number of shrinkage parameters needed, and creates a framework for sharing information across units of interest. We show that this approach leads to more biologically meaningful and interpretable results in our brain imaging problem, since it allows the discrimination between active and inactive regions, while at the same time ranking the discoveries into clusters representing tiers of similar importance.