A Novel Sparse Compositional Technique Reveals Microbial Perturbations

Authors

Cameron Martino, University of California San Diego
James T. Morton, University of California San DiegoFollow
Clarisse A. Martoz, University of California San Diego
Luke R. Thompson, University of Southern MississippiFollow
Anupriya Tripathi, University of California San DiegoFollow
Rob Knight, University of California San DiegoFollow
Karsten Zengler, University of California San DiegoFollow

Document Type

Article

Publication Date

1-1-2019

Department

Biological Sciences

School

Biological, Environmental, and Earth Sciences

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.

Comments

Published by mSystems at 10.1128/msystems.00016-19.

Publication Title

mSystems

Volume

4

Issue

1

Recommended Citation

Martino, C., Morton, J. T., Martoz, C. A., Thompson, L. R., Tripathi, A., Knight, R., Zengler, K. (2019). A Novel Sparse Compositional Technique Reveals Microbial Perturbations. mSystems, 4(1).
Available at: https://aquila.usm.edu/fac_pubs/17485