Wagner Brandie D, Grunwald Gary K, Zerbe Gary O, Mikulich-Gilbertson Susan K, Robertson Charles E, Zemanick Edith T, Harris J Kirk
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States.
Department of Pediatrics, School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States.
Front Microbiol. 2018 May 22;9:1037. doi: 10.3389/fmicb.2018.01037. eCollection 2018.
Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. As substantial variability in microbiota communities is seen across subjects, the use of longitudinal study designs is important to better understand variation of the microbiome within individual subjects. Complex study designs with longitudinal sample collection require analytic approaches to account for this additional source of variability. A common approach to assessing community changes is to evaluate the change in alpha diversity (the variety and abundance of organisms in a community) over time. However, there are several commonly used alpha diversity measures and the use of different measures can result in different estimates of magnitude of change and different inferences. It has recently been proposed that diversity profile curves are useful for clarifying these differences, and may provide a more complete picture of the community structure. However, it is unclear how to utilize these curves when interest is in evaluating changes in community structure over time. We propose the use of a bi-exponential function in a longitudinal model that accounts for repeated measures on each subject to compare diversity profiles over time. Furthermore, it is possible that no change in alpha diversity (single community/sample) may be observed despite the presence of a highly divergent community composition. Thus, it is also important to use a beta diversity measure (similarity between multiple communities/samples) that captures changes in community composition. Ecological methods developed to evaluate temporal turnover have currently only been applied to investigate changes of a single community over time. We illustrate the extension of this approach to multiple communities of interest (i.e., subjects) by modeling the beta diversity measure over time. With this approach, a rate of change in community composition is estimated. There is a need for the extension and development of analytic methods for longitudinal microbiota studies. In this paper, we discuss different approaches to model alpha and beta diversity indices in longitudinal microbiota studies and provide both a review of current approaches and a proposal for new methods.
随着高通量测序技术的出现，识别存在于人类相关微生物群落中的大多数生物体变得可行。由于不同个体的微生物群群落存在很大差异，采用纵向研究设计对于更好地理解个体受试者体内微生物组的变化非常重要。具有纵向样本收集的复杂研究设计需要分析方法来考虑这种额外的变异性来源。评估群落变化的一种常用方法是评估随时间变化的α多样性（群落中生物体的种类和丰度）。然而，有几种常用的α多样性测量方法，使用不同的测量方法可能会导致变化幅度的不同估计和不同的推断。最近有人提出，多样性轮廓曲线有助于澄清这些差异，并可能提供更完整的群落结构图景。然而，当关注的是评估群落结构随时间的变化时，尚不清楚如何利用这些曲线。我们建议在纵向模型中使用双指数函数，该函数考虑了对每个受试者的重复测量，以便比较随时间变化的多样性轮廓。此外，尽管群落组成存在高度差异，但可能观察不到α多样性（单个群落/样本）的变化。因此，使用能够捕捉群落组成变化的β多样性测量方法（多个群落/样本之间的相似性）也很重要。目前，为评估时间周转而开发的生态方法仅应用于研究单个群落随时间的变化。我们通过对随时间变化的β多样性测量进行建模，说明了将这种方法扩展到多个感兴趣的群落（即受试者）的情况。通过这种方法，可以估计群落组成的变化率。纵向微生物群研究的分析方法需要扩展和发展。在本文中，我们讨论了纵向微生物群研究中对α和β多样性指数进行建模的不同方法，并对当前方法进行了综述，同时提出了新方法的建议。
