MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of <i>Andropogon gerardii</i>, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.生物信息学领域的新近进展使得高通量微生物组数据得以广泛获取，而当下亟需新型统计工具以最大化从这类数据中挖掘的信息价值。例如，针对受控实验所得的高维微生物组数据开展分析，仍是微生物组研究中有待深入探索的前沿方向。当前主流分析方法多基于概括微生物组整体属性的统计量开展研究，但这类数据降维处理手段会使得我们无法对环境刺激作用于单个微生物类群的精细尺度效应进行统计推断。另有部分方法将单个微生物类群的相对丰度或计数作为响应变量纳入混合效应模型，但这类方法未能考量微生物群落间存在的复杂关联模式。本文提出一种新型贝叶斯混合效应模型，可利用微生物组内部的跨类群关联关系，我们将该模型命名为微生物组混合模型（microbiome mixed model, MIMIX）。MIMIX可实现多项分析功能：包括处理效应的全局检验、针对单个微生物类群的局部检验与处理效应估计、量化异质性来源对微生物组变异的相对贡献，以及识别微生物组中潜在的生态亚群落。MIMIX专为大型微生物组实验设计，结合了贝叶斯因子分析以高效表征类群间的依赖关系，同时采用贝叶斯变量选择方法以实现模型稀疏性。本文通过模拟实验与一项2×2析因实验验证了该模型的有效性：该析因实验探究了营养添加与草食动物排除对多年生丛生禾草须芒草（Andropogon gerardii）叶际真菌微生物组的影响，且该实验作为全球营养网络（Nutrient Network）研究计划的一部分开展。本文的补充材料（包含可用于复现研究工作的标准化材料说明）可作为在线补充资源获取。