KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.
KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.
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DOI：
10.1214/17-aoas1102
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Shojaie A
中科院分区：
文献类型：
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作者：
Randolph TW;Zhao S;Copeland W;Hullar M;Shojaie A
人类微生物组数据的分析通常基于降维图形显示和源自每个样本中微生物丰度向量的聚类。这些排序方法的共同点是使用基于生物学的相似性定义。特别是，主坐标分析通常使用生态定义的距离进行，允许分析纳入上下文相关的非欧几里得结构。在本文中，我们超越了降维排序方法，并描述了扩展这些基于距离的方法的高维回归模型框架。特别是，我们使用基于内核的方法来展示如何将各种外在信息（例如系统发育）纳入惩罚回归模型中，以估计与表型或临床结果的分类特异性关联。此外，我们展示了如何使用该回归框架来解决由相对丰度组成的多元预测变量的组成性质；也就是说，其条目之和为常数的向量。我们使用最近两项关于肠道和阴道微生物组的研究的数据通过多次模拟来说明这种方法。最后，我们对自己的数据进行了应用，其中我们还对代表微生物丰度和脂肪百分比之间关联的估计系数进行了显着性检验。
The analysis of human microbiome data is often based on dimension-reduced graphical displays and clusterings derived from vectors of microbial abundances in each sample. Common to these ordination methods is the use of biologically motivated definitions of similarity. Principal coordinate analysis, in particular, is often performed using ecologically defined distances, allowing analyses to incorporate context-dependent, non-Euclidean structure. In this paper, we go beyond dimension-reduced ordination methods and describe a framework of high-dimensional regression models that extends these distance-based methods. In particular, we use kernel-based methods to show how to incorporate a variety of extrinsic information, such as phylogeny, into penalized regression models that estimate taxonspecific associations with a phenotype or clinical outcome. Further, we show how this regression framework can be used to address the compositional nature of multivariate predictors comprised of relative abundances; that is, vectors whose entries sum to a constant. We illustrate this approach with several simulations using data from two recent studies on gut and vaginal microbiomes. We conclude with an application to our own data, where we also incorporate a significance test for the estimated coefficients that represent associations between microbial abundance and a percent fat.