A Novel Sparse Compositional Technique Reveals Microbial Perturbations

ABSTRACT The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normalit...

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Autores principales:	Cameron Martino, James T. Morton, Clarisse A. Marotz, Luke R. Thompson, Anupriya Tripathi, Rob Knight, Karsten Zengler
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	compositional computational biology matrix completion microbiome metagenomics Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/cfc52501a8ce431dbf3c609fdbe467d3
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spelling	oai:doaj.org-article:cfc52501a8ce431dbf3c609fdbe467d32021-12-02T18:39:15ZA Novel Sparse Compositional Technique Reveals Microbial Perturbations10.1128/mSystems.00016-192379-5077https://doaj.org/article/cfc52501a8ce431dbf3c609fdbe467d32019-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00016-19https://doaj.org/toc/2379-5077ABSTRACT The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/deicode/.Cameron MartinoJames T. MortonClarisse A. MarotzLuke R. ThompsonAnupriya TripathiRob KnightKarsten ZenglerAmerican Society for Microbiologyarticlecompositionalcomputational biologymatrix completionmicrobiomemetagenomicsMicrobiologyQR1-502ENmSystems, Vol 4, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	compositional computational biology matrix completion microbiome metagenomics Microbiology QR1-502
spellingShingle	compositional computational biology matrix completion microbiome metagenomics Microbiology QR1-502 Cameron Martino James T. Morton Clarisse A. Marotz Luke R. Thompson Anupriya Tripathi Rob Knight Karsten Zengler A Novel Sparse Compositional Technique Reveals Microbial Perturbations
description	ABSTRACT The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/deicode/.
format	article
author	Cameron Martino James T. Morton Clarisse A. Marotz Luke R. Thompson Anupriya Tripathi Rob Knight Karsten Zengler
author_facet	Cameron Martino James T. Morton Clarisse A. Marotz Luke R. Thompson Anupriya Tripathi Rob Knight Karsten Zengler
author_sort	Cameron Martino
title	A Novel Sparse Compositional Technique Reveals Microbial Perturbations
title_short	A Novel Sparse Compositional Technique Reveals Microbial Perturbations
title_full	A Novel Sparse Compositional Technique Reveals Microbial Perturbations
title_fullStr	A Novel Sparse Compositional Technique Reveals Microbial Perturbations
title_full_unstemmed	A Novel Sparse Compositional Technique Reveals Microbial Perturbations
title_sort	novel sparse compositional technique reveals microbial perturbations
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/cfc52501a8ce431dbf3c609fdbe467d3
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A Novel Sparse Compositional Technique Reveals Microbial Perturbations

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