Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.

Automated summary

Human microbiome research benefits from characterizing microbial networks, which can reveal key microbes for beneficial health effects. This study demonstrates the potential of wavelet clustering, a technique that clusters time series based on their spectral characteristics. The researchers apply this technique to densely sampled human gut microbiome time series and compare the results with traditional correlation-based methods, showing significant differences in terms of clustered elements, branching structure, and branch length. Wavelet clustering capitalizes on the dynamic nature of the human microbiome, revealing community structures that correlation-based methods may overlook.