Multivariate statistical monitoring system for microbial population dynamics
Multivariate statistical monitoring system for microbial population dynamics
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DOI：
10.1088/1478-3975/ac3ad6
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发表时间：
2021-11
期刊：
影响因子：
2
通讯作者：
Koji Ishiya;S. Aburatani
中科院分区：
文献类型：
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作者：
Koji Ishiya;S. Aburatani
其自然环境中的微生物群随着环境条件的变化而动态变化。对微生物种群中这些动态变化的检测对于理解环境变化对微生物群落的影响至关重要。在这里，我们提出了一种新的方法来检测微生物组的时间序列变化，基于多变量统计过程控制。通过关注物种间的结构，这种方法能够稳健地检测由大量微生物物种组成的微生物群落中的时间序列变化。将这种方法应用于经验性的人类肠道微生物组数据，我们准确地跟踪了饮食干预试验引起的微生物区系组成的时间序列变化。这种方法对于跟踪干预后的恢复过程也是很好的。我们的方法可用于监测复杂微生物群落的动态变化。
Microbiomes in their natural environments vary dynamically with changing environmental conditions. The detection of these dynamic changes in microbial populations is critical for understanding the impact of environmental changes on the microbial community. Here, we propose a novel method to detect time-series changes in the microbiome, based on multivariate statistical process control. By focusing on the interspecies structures, this approach enables the robust detection of time-series changes in a microbiome composed of a large number of microbial species. Applying this approach to empirical human gut microbiome data, we accurately traced time-series changes in microbiota composition induced by a dietary intervention trial. This method was also excellent for tracking the recovery process after the intervention. Our approach can be useful for monitoring dynamic changes in complex microbial communities.