Robust and automatic definition of microbiome states
Robust and automatic definition of microbiome states
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DOI：
10.7717/peerj.6657
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发表时间：
2019-03-26
期刊：
影响因子：
2.7
通讯作者：
Wilkinson, Mark D.
中科院分区：
文献类型：
--
作者：
Garcia-Jimenez, Beatriz;Wilkinson, Mark D.
对微生物组动力学的分析将有助于阐明在各种生物学或经济重要情况下微生物群落进化的模式；然而，目前这在一定程度上受到了阻碍，因为缺乏严格、正式且普遍适用的方法来辨别复杂微生物种群的不同结构。在人口规模上定义微生物组“群落状态类型”的聚类方法已被广泛使用，但尚未标准化。同样，状态类型内的不同变化也有详细记录，但没有严格的方法来区分社区结构中这些更微妙的变化。最后，在纵向数据等中可能会发现差异较小的个体间差异，并将与疾病与健康等重要特征相关。我们提出了一种自动化、通用、客观、独立于领域和内部验证的程序，以在包含任何程度的系统发育多样性的数据集中定义统计上不同的微生物组状态。状态识别的鲁棒性是通过结合多种稳定集群验证技术来客观建立的。为了证明我们的方法即使在包含高度相似的细菌群落的数据集中也能检测离散状态的有效性，并证明我们的方法的广泛适用性，我们重复使用了来自各种生态位和物种的八个不同的纵向微生物组数据集。我们还通过提供不同的分类单元子集作为聚类输入来证明我们的算法的灵活性，证明它可以在过滤或未过滤的数据上以及在一系列不同的分类级别上运行。最终输出是一组严格定义的状态，然后可以将其用作通用生物标志物，用于各种下游目的，例如与疾病相关、监测对干预的反应或识别最佳表现群体。
Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome "community statetypes" at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, infra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm's flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.