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Title: A Novel Sparse Compositional Technique Reveals Microbial Perturbations

Abstract

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 performmore » 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/q2-deicode .« less

Authors:
; ; ; ORCiD logo; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1494211
Grant/Contract Number:  
SC0012658; SC0012586
Resource Type:
Journal Article: Published Article
Journal Name:
mSystems
Additional Journal Information:
Journal Name: mSystems Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2379-5077
Publisher:
American Society for Microbiology
Country of Publication:
United States
Language:
English

Citation Formats

Martino, Cameron, Morton, James T., Marotz, Clarisse A., Thompson, Luke R., Tripathi, Anupriya, Knight, Rob, Zengler, Karsten, and Neufeld, ed., Josh D. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. United States: N. p., 2019. Web. doi:10.1128/mSystems.00016-19.
Martino, Cameron, Morton, James T., Marotz, Clarisse A., Thompson, Luke R., Tripathi, Anupriya, Knight, Rob, Zengler, Karsten, & Neufeld, ed., Josh D. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. United States. doi:10.1128/mSystems.00016-19.
Martino, Cameron, Morton, James T., Marotz, Clarisse A., Thompson, Luke R., Tripathi, Anupriya, Knight, Rob, Zengler, Karsten, and Neufeld, ed., Josh D. Tue . "A Novel Sparse Compositional Technique Reveals Microbial Perturbations". United States. doi:10.1128/mSystems.00016-19.
@article{osti_1494211,
title = {A Novel Sparse Compositional Technique Reveals Microbial Perturbations},
author = {Martino, Cameron and Morton, James T. and Marotz, Clarisse A. and Thompson, Luke R. and Tripathi, Anupriya and Knight, Rob and Zengler, Karsten and Neufeld, ed., Josh D.},
abstractNote = {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/q2-deicode .},
doi = {10.1128/mSystems.00016-19},
journal = {mSystems},
issn = {2379-5077},
number = 1,
volume = 4,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1128/mSystems.00016-19

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Works referenced in this record:

Probabilistic Principal Component Analysis
journal, August 1999

  • Tipping, Michael E.; Bishop, Christopher M.
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 61, Issue 3, p. 611-622
  • DOI: 10.1111/1467-9868.00196