Department of Physics, University of Florida, Gainesville, FL, 32611, USA.
Physician-Scientist Training Pathway, Department of Medicine, UCSD, San Diego, CA, 92103, USA.
NPJ Syst Biol Appl. 2023 Jun 20;9(1):26. doi: 10.1038/s41540-023-00285-6.
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.
降维分析通过利用相似生态扰动驱动的多种细菌的集体丰度波动，为高维微生物组动态提供了独特的见解。然而，目前还没有提供微生物组动态的低维表示的方法，无论是在群落水平还是个体分类群水平。为此，我们提出了 EMBED：essentialMicroBiomEDynamics，这是一种概率非线性张量分解方法。与结构生物物理学中的正常模式分析类似，EMBED 推断出生态正常模式（ECN），它代表了捕捉微生物群落集体行为的独特正交模式。使用多个真实和合成数据集，我们表明，少量的 ECN 可以准确地近似微生物组动态。推断出的 ECN 反映了特定的生态行为，为个体细菌的动态可以划分的自然模板提供了参考。此外，EMBED 中的多主题处理系统地识别了传统方法无法检测到的特定于主题和普遍的丰度动态。总的来说，这些结果强调了 EMBED 作为微生物组动态研究的多功能降维工具的实用性。
