A nonparametric spatial test to identify factors that shape a microbiome

TL;DR

This paper introduces a Bayesian spatial model for microbiome data that accounts for spatial dependence and cross-taxa relationships, improving factor identification over traditional methods.

Contribution

It develops a novel nonparametric Bayesian model that captures spatial effects and cross-dependence in microbiome presence-absence data, enhancing variable selection accuracy.

Findings

Simulation shows improved variable selection with spatial dependence.

Traditional distance-based tests often fail to maintain size under spatial effects.

Application to US homes' fungal communities demonstrates practical utility.

Abstract

The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found with homes across the contiguous United States.