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      A Novel Sparse Compositional Technique Reveals Microbial Perturbations

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          Abstract

          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.

          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/.

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          Most cited references26

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          Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information

          The move from OTU-based to sOTU-based analysis, while providing additional resolution, also introduces computational challenges. We demonstrate that one popular method of dealing with sOTUs (building a de novo tree from the short sequences) can provide incorrect results in human gut metagenomic studies and show that phylogenetic placement of the new sequences with SEPP resolves this problem while also yielding other benefits over existing methods.
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            Biplots of compositional data

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              QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

              We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msys
                msys
                mSystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                12 February 2019
                Jan-Feb 2019
                : 4
                : 1
                : e00016-19
                Affiliations
                [a ]Department of Pediatrics, University of California San Diego, La Jolla, California, USA
                [b ]Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, USA
                [c ]Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
                [d ]Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA
                [e ]Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
                [f ]Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
                [g ]Department of Bioengineering, University of California San Diego, La Jolla, California, USA
                University of Waterloo
                Author notes
                Address correspondence to Karsten Zengler, kzengler@ 123456ucsd.edu .

                Citation Martino C, Morton JT, Marotz CA, Thompson LR, Tripathi A, Knight R, Zengler K. 2019. A novel sparse compositional technique reveals microbial perturbations. mSystems 4:e00016-19. https://doi.org/10.1128/mSystems.00016-19.

                Author information
                https://orcid.org/0000-0002-3911-1280
                Article
                mSystems00016-19
                10.1128/mSystems.00016-19
                6372836
                30801021
                2cbcdefb-5b65-4216-a26e-a443f55fab56
                Copyright © 2019 Martino et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 9 January 2019
                : 11 January 2019
                Page count
                supplementary-material: 5, Figures: 5, Tables: 0, Equations: 12, References: 54, Pages: 13, Words: 7804
                Funding
                Funded by: HHS | National Institutes of Health (NIH), https://doi.org/10.13039/100000002;
                Award ID: AR071731
                Award Recipient :
                Funded by: National Science Foundation (NSF), https://doi.org/10.13039/100000001;
                Award ID: 1332344
                Award Recipient :
                Funded by: National Science Foundation (NSF), https://doi.org/10.13039/100000001;
                Award ID: 1144086
                Award Recipient :
                Funded by: U.S. Department of Energy (DOE), https://doi.org/10.13039/100000015;
                Award ID: DE-SC0012658
                Award Recipient :
                Funded by: U.S. Department of Energy (DOE), https://doi.org/10.13039/100000015;
                Award ID: DE-SC0012586
                Award Recipient :
                Categories
                Research Article
                Ecological and Evolutionary Science
                Custom metadata
                January/February 2019

                compositional,computational biology,matrix completion,microbiome,metagenomics

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