Abstract
Time-series can provide critical insights into the structure and function of microbial communities. The analysis of temporal data warrants statistical considerations, distinct from comparative microbiome studies, to address ecological questions. This primer identifies unique challenges and approaches for analyzing microbiome time-series. In doing so, we focus on (1) identifying compositionally similar samples, (2) inferring putative interactions among populations, and (3) detecting periodic signals. We connect theory, code and data via a series of hands-on modules with a motivating biological question centered on marine microbial ecology. The topics of the modules include characterizing shifts in community structure and activity, identifying expression levels with a diel periodic signal, and identifying putative interactions within a complex community. Modules are presented as self-contained, open-access, interactive tutorials in R and Matlab. Throughout, we highlight statistical considerations for dealing with autocorrelated and compositional data, with an eye to improving the robustness of inferences from microbiome time-series. In doing so, we hope that this primer helps to broaden the use of time-series analytic methods within the microbial ecology research community.
Highlights
Microbiomes encompass biological complexity from molecules to genes, metabolisms, and community ecological interactions
The extent to which these daily cycles and the timings of particular metabolic activities extend to protistan members of the North Pacific Subtropical Gyre (NPSG) surface ecosystem remains less characterized
Based on the observation of sample differentiation between the middle of the day (2 PM) and dawn (6 AM) from exploratory ordination and clustering analyses described in 4.1, we further investigated the hypothesis that some protists may exhibit a 24-h periodicity in their 18S rRNA gene expression levels
Summary
Microbiomes encompass biological complexity from molecules to genes, metabolisms, and community ecological interactions. It is possible to sample at the time-scale at which those processes occur, resulting in the collection of microbiome time-series data. While such high-resolution sampling opens new avenues of inquiry, it presents new challenges for analysis (McMurdie and Holmes, 2014; Weiss et al, 2016, 2017; Widder et al, 2016; Knight et al, 2018). Some definition of taxa is often necessary for characterizing the composition of microbial communities In this primer, we use the term taxon to denote approximately species-level designations, such as operational taxonomic unit (OTU) or amplicon sequence variant (ASV)
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