A Novel Sparse Compositional Technique Reveals Microbial Perturbations
A Novel Sparse Compositional Technique Reveals Microbial Perturbations
复制标题
DOI：
10.1128/msystems.00016-19
复制
发表时间：
2019-01-01
期刊：
影响因子：
6.4
通讯作者：
Zengler, Karsten
中科院分区：
文献类型：
--
作者：
Martino, Cameron;Morton, James T.;Zengler, Karsten
许多宿主或环境微生物组研究的中心目标是阐明与微生物群落组成相关的因素，并将微生物特征与结果联系起来。然而，由于微生物组数据集的高维性、非正态性、稀疏性和组成性质，这些目标往往变得复杂。微生物组分析的一个关键工具是β多样性，由微生物样本之间的距离定义。人们提出了许多不同的距离度量，它们对具有不同特征的数据具有不同的区分能力。在这里，我们提出了一种基于中心对数比变换和矩阵补全的组合多样性度量，称为鲁棒艾奇逊主成分分析法。我们通过模拟证明了合成变换在β多样性计算上游的好处。此外，我们证明了在真实微生物组数据集的几个减少的样本子集上，与当前方法相比，提高了效应大小、分类准确性和对测序深度的鲁棒性。最后，我们强调了这种新的beta多样性度量保留与样本排序相关的特征负载的能力，揭示了显著的群落间生态位特征的重要性。考虑到微生物组数据集的稀疏组成特性，鲁棒的艾奇逊主成分分析可以在微生物生态位之间产生高判别能力和显著特征排序。执行此分析的软件可以在开源许可下获得，可以从https://github.com/biocore/DEICODE获得；此外，在https://library.qiime2.org/plugins/deicode/上提供了QIIME 2插件来执行此分析。
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/.