Physics Department, Bar-Ilan University, Ramat Gan, Israel.
PLoS One. 2024 May 30;19(5):e0301683. doi: 10.1371/journal.pone.0301683. eCollection 2024.
The human microbiome plays a crucial role in determining our well-being and can significantly influence human health. The individualized nature of the microbiome may reveal host-specific information about the health state of the subject. In particular, the microbiome is an ecosystem shaped by a tangled network of species-species and host-species interactions. Thus, analysis of the ecological balance of microbial communities can provide insights into these underlying interrelations. However, traditional methods for network analysis require many samples, while in practice only a single-time-point microbial sample is available in clinical screening. Recently, a method for the analysis of a single-time-point sample, which evaluates its 'network impact' with respect to a reference cohort, has been applied to analyze microbial samples from women with Gestational Diabetes Mellitus. Here, we introduce different variations of the network impact approach and systematically study their performance using simulated 'samples' fabricated via the Generalized Lotka-Volttera model of ecological dynamics. We show that the network impact of a single sample captures the effect of the interactions between the species, and thus can be applied to anomaly detection of shuffled samples, which are 'normal' in terms of species abundance but 'abnormal' in terms of species-species interrelations. In addition, we demonstrate the use of the network impact in binary and multiclass classifications, where the reference cohorts have similar abundance profiles but different species-species interactions. Individualized analysis of the human microbiome has the potential to improve diagnosis and personalized treatments.
人类微生物组在决定我们的健康方面起着至关重要的作用，并可能对人类健康产生重大影响。微生物组的个体性可能揭示出有关主体健康状况的宿主特异性信息。特别是，微生物组是一个由物种-物种和宿主-物种相互作用交织而成的生态系统。因此，对微生物群落生态平衡的分析可以深入了解这些潜在的相互关系。然而，传统的网络分析方法需要许多样本，而在实际临床筛选中，通常只有一个时间点的微生物样本可用。最近，已经应用了一种分析单点样本的方法，该方法通过与参考队列的“网络影响”来评估其对微生物样本的影响，用于分析患有妊娠糖尿病的女性的微生物样本。在这里，我们介绍了网络影响方法的不同变体，并使用通过广义Lotka-Volterra 生态动力学模型生成的模拟“样本”系统地研究了它们的性能。我们表明，单个样本的网络影响捕捉到了物种间相互作用的影响，因此可以应用于随机化样本的异常检测，这些样本在物种丰度方面是“正常”的，但在物种-物种相互关系方面是“异常”的。此外，我们展示了网络影响在二进制和多类分类中的应用，其中参考队列具有相似的丰度分布，但物种-物种相互作用不同。对人类微生物组的个体化分析有可能改善诊断和个性化治疗。
