Dimensionality reduction can offer unique insights into high dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa level are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic non-linear tensor factorization approach. Similar to normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. A very small number of ECNs can accurately approximate microbiome dynamics across multiple data sets. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.
The gut microbiome is well-established to be a significant driver of host health and disease. Longitudinal studies involving high-throughput sequencing technologies have begun to unravel the complex dynamics of these ecosystems, and quantitative frameworks are now being developed to better understand their organizing principles. Dimensionality reduction can offer unique insights into gut bacterial dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar underlying ecological factors. However, methods providing lower-dimensional representations of gut microbial dynamics both at the community and individual taxa level are currently missing. To that end, we develop EMBED: Essential Microbiome Dynamics. Similar to normal modes in structural biology, EMBED infers ecological normal modes (ECNs), which represent the unique set of orthogonal dynamical trajectories capturing the collective behavior of a community. We show that a small number of ECNs accurately describe gut microbiome dynamics across data sets that encompass dietary changes and antibiotic-related perturbations. Importantly, we find that ECNs often reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Collectively, our results highlight the utility of dimensionality reduction approaches to understanding the dynamics of the gut microbiome and provide a framework to study the dynamics of other high-dimensional systems as well.
Dimensionality reduction offers unique insights into high-dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa levels are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic nonlinear tensor factorization approach. Like normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. Using multiple real and synthetic datasets, we show that a very small number of ECNs can accurately approximate microbiome dynamics. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.