Wang Chan, Hu Jiyuan, Blaser Martin J, Li Huilin
Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016, NY, USA.
Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, 08854-8021, NJ, USA.
BMC Genomics. 2021 Sep 15;22(1):667. doi: 10.1186/s12864-021-07948-w.
The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time.
We propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice.
The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.
人类微生物组具有内在的动态性，其动态特性在维持健康和引发疾病方面起着关键作用。随着纵向微生物组研究数量的不断增加，科学家们渴望了解微生物动态的全面特征及其对健康和疾病相关表型的影响。然而，由于纵向微生物组数据结构具有挑战性，用于表征微生物随时间动态变化的分析方法很少。
我们针对基于高维度和系统发育的纵向微生物组数据提出了一种微生物趋势分析（MTA）框架。具体而言，MTA 可以执行三项任务：1）在群落水平上捕捉一组受试者的常见微生物动态趋势，并识别优势分类群；2）检查组间微生物总体动态趋势是否存在显著差异；3）根据个体受试者的纵向微生物图谱对其进行分类。我们广泛的模拟表明，所提出的 MTA 框架在假设检验、分类群识别和受试者分类方面具有稳健性和强大功能。我们的实际数据分析通过对小鼠的纵向研究进一步说明了 MTA 的实用性。
所提出的 MTA 框架是纵向微生物组研究中用于调查动态微生物模式的一种有吸引力且有效的工具。
