Abstract
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.
Highlights
Identification of the majority of organisms present in humanassociated microbial communities is feasible with the advent of high throughput sequencing technology
Current approaches include extending models applied to individual taxa to address repeated measures over time (Chen and Li, 2016; Fang et al, 2016; Wagner et al, 2017) but not much attention has been given to discussion and extension of ecological community indices, which are useful for describing the community biodiversity
The recently proposed alpha diversity curves are explained in section Alpha Diversity Curves and we develop a hierarchical model approach to analyze these curves longitudinally with a non-linear mixed model
Summary
Identification of the majority of organisms present in humanassociated microbial communities is feasible with the advent of high throughput sequencing technology. It is important to use a beta diversity measure (similarity between multiple communities/samples) that captures changes in community composition.
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