Time series
Microbial communities are dynamic, constantly evolving over time. However, despite temporal fluctuations driven by environmental and other factors, the microbiome retains its core characteristics (Schmidt, Raes, and Bork 2018).
Cross-sectional studies capture the microbiome at a single time point. In contrast, longitudinal (temporal) study designs follow the same individuals over time. This allows researchers to study how microbial communities change, for example, in response to an intervention such as antibiotic or probiotic treatment, or during the progression of a disease.
Longitudinal data also help to interpret cross-sectional findings. A single sample reflects not only differences between individuals but also the natural day-to-day variability within each individual’s microbiome. By sampling the same subjects repeatedly, this within-subject variability can be estimated and separated from between-subject differences. This reduces the uncertainty in comparisons between groups.
Analyzing longitudinal data includes aspects that need to be taken into account, such as the dependence between samples collected from the same subject.
Most of the methods introduced earlier in this book can be applied to longitudinal data with small modifications. For more details, see the following chapters:
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