A Novel Sparse Compositional Technique Reveals Microbial Perturbations

By Cameron Martino, James T. Morton, C. Marotz, Luke R. Thompson, A. Tripathi, Rob Knight, Karsten Zengler. mSystems, 2019

467 citations recorded by the source corpus.

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/.

Subfields: Molecular Biology, Periodontics

View Paper: https://www.semanticscholar.org/paper/54f55ab3447de0893e881fa828ede46673b06d2a

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In Molecular Biology

Citations in the corpus

QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data (2020, methodology): “This section is devoted to longitudinal microbiome analysis using the q2-longitudinal plugin (Bokulich, Estaki et al.”