Traditionally, human microbiology has been strongly built on the laboratory focused culture of microbes isolated from human specimens in patients with acute or chronic infection. These approaches primarily view human disease through the lens of a single species and its relevant clinical setting however such approaches fail to account for the surrounding environment and wide microbial diversity that exists Given the emergence of next generation sequencing technologies and advancing bioinformatic pipelines, researchers now have unprecedented capabilities to characterise the human microbiome in terms of its taxonomy, function, antibiotic resistance and even bacteriophages. Despite this, an analysis of microbial communities has largely been restricted to ordination, ecological measures, and discriminant taxa analysis. This is predominantly due to a lack of suitable computational tools to facilitate microbiome analytics. In this review, we first evaluate the key concerns related to the inherent structure of microbiome datasets which include its compositionality and batch effects. We describe the available and emerging analytical techniques including integrative analysis, machine learning, microbial association networks, topological data analysis (TDA) and mathematical modelling. We also present how these methods may translate to clinical settings including tools for implementation. Mathematical based analytics for microbiome analysis represents a promising avenue for clinical translation across a range of acute and chronic disease states.

中文翻译：

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用于临床转化的基于数学的微生物组分析


传统上，人类微生物学很大程度上建立在从急性或慢性感染患者的人体标本中分离出微生物的实验室重点培养基础上。这些方法主要通过单一物种及其相关临床环境的视角来观察人类疾病，但这些方法无法考虑周围环境和存在的广泛微生物多样性。鉴于下一代测序技术的出现和先进的生物信息学管道，研究人员现在已经在分类、功能、抗生素耐药性甚至噬菌体方面表征人类微生物组的前所未有的能力。尽管如此，对微生物群落的分析在很大程度上仅限于排序、生态测量和判别类群分析。这主要是由于缺乏合适的计算工具来促进微生物组分析。在这篇综述中，我们首先评估与微生物组数据集固有结构相关的关键问题，包括其组成和批次效应。我们描述了可用的和新兴的分析技术，包括综合分析、机器学习、微生物关联网络、拓扑数据分析 (TDA) 和数学建模。我们还介绍了这些方法如何转化为临床环境，包括实施工具。基于数学的微生物组分析为一系列急性和慢性疾病状态的临床转化提供了一条有前途的途径。