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，而为最大化挖掘此类数据所蕴含的信息，亟需开发全新的统计分析工具。例如，针对受控设计实验产生的高维微生物组数据的分析，仍是微生物组研究中有待攻克的研究前沿之一。当前主流分析方法多基于汇总微生物组整体特征的统计量，但这类降维操作会导致无法推断环境刺激对单个微生物类群的精细调控效应。另有部分研究将单个类群的丰度比例或计数作为响应变量纳入混合效应模型，但此类方法未能考量微生物群落间复杂的关联模式。本文提出一种全新的贝叶斯混合效应模型，可充分利用微生物组内类群间的跨类群相关性，我们将该模型命名为微生物组混合模型（microbiome mixed model, MIMIX）。MIMIX可实现多项核心分析功能：包括处理效应的全局检验、针对单个类群的处理效应局部检验与效应估计、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。针对大规模微生物组实验场景，MIMIX结合贝叶斯因子分析以高效刻画类群间的依赖关系，并辅以贝叶斯变量选择方法实现模型稀疏性约束。我们通过模拟实验与一项2×2析因实验验证了该模型的性能：该析因实验探究了养分添加与草食动物排除对大须芒草（*Andropogon gerardii*，一种多年生丛生禾草）叶际真菌微生物组的影响，该研究作为全球养分网络（Nutrient Network）研究计划的组成部分。本文的补充材料（含可复现研究所需材料的标准化说明）可通过在线补充资源获取。