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December 2019 A nonparametric spatial test to identify factors that shape a microbiome
Susheela P. Singh, Ana-Maria Staicu, Robert R. Dunn, Noah Fierer, Brian J. Reich
Ann. Appl. Stat. 13(4): 2341-2362 (December 2019). DOI: 10.1214/19-AOAS1262


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 within homes across the contiguous United States.


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Susheela P. Singh. Ana-Maria Staicu. Robert R. Dunn. Noah Fierer. Brian J. Reich. "A nonparametric spatial test to identify factors that shape a microbiome." Ann. Appl. Stat. 13 (4) 2341 - 2362, December 2019.


Received: 1 February 2018; Revised: 1 April 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160942
MathSciNet: MR4037433
Digital Object Identifier: 10.1214/19-AOAS1262

Keywords: Bayesian nonparametrics , Dirichlet process , high dimensional data , spatial modeling , spike-and-slab prior , Variable selection

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 4 • December 2019
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