Silverman Justin D, Washburne Alex D, Mukherjee Sayan, David Lawrence A
Program in Computational Biology and Bioinformatics, Duke University, Durham, United States.
Medical Scientist Training Program, Duke University, Durham, United States.
Elife. 2017 Feb 15;6:e21887. doi: 10.7554/eLife.21887.
Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities.
对微生物群落（微生物群）的调查通常以物种的相对丰度来衡量，这些调查已经阐明了这些群落在人类健康和疾病中的重要性。然而，统计假象通常困扰着相对丰度数据的分析。在这里，我们引入了PhILR变换，它将微生物进化模型与等距对数比变换相结合，使现成的统计工具能够安全地应用于微生物群调查。我们证明，群落水平结构的分析可以应用于PhILR变换后的数据，其在基准测试中的表现可与标准工具相媲美或超越标准工具。此外，通过在PhILR变换空间中分解距离，我们确定了可能已适应不同人体部位的相邻进化枝。分解方差表明，人体部位内细菌进化枝的协变随着系统发育相关性的增加而增加。总之，这些发现说明了PhILR变换如何结合统计和系统发育模型来克服成分数据挑战，并获得与微生物群落相关的进化见解。
