Format

Send to

Choose Destination
mSystems. 2019 Feb 12;4(1). pii: e00016-19. doi: 10.1128/mSystems.00016-19. eCollection 2019 Jan-Feb.

A Novel Sparse Compositional Technique Reveals Microbial Perturbations.

Author information

1
Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
2
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, USA.
3
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.
4
Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA.
5
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.
6
Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA.
7
Department of Bioengineering, University of California San Diego, La Jolla, California, USA.

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/.

KEYWORDS:

compositional; computational biology; matrix completion; metagenomics; microbiome

Supplemental Content

Full text links

Icon for American Society for Microbiology Icon for PubMed Central
Loading ...
Support Center