Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks
Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks
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DOI：
10.1214/21-aoas1598
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Hee Cheol Chung;Irina Gaynanova;Yang Ni
中科院分区：
文献类型：
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作者：
Hee Cheol Chung;Irina Gaynanova;Yang Ni
微生物对宿主的健康起着至关重要的作用。高通量测序技术的进步为更深入地了解微生物相互作用提供了机会。然而，由于16S核糖体RNA测序的限制，微生物组数据是零膨胀的，不能跨受试者对微生物丰度进行定量比较。通过利用最近的微生物群谱技术来量化16S核糖体RNA微生物计数，我们提出了一个新的贝叶斯图形模型，该模型通过系统发育树先前结合了微生物的进化历史，并使用截断的高斯Copula显式地解释了零通货膨胀。仿真研究表明，进化信息显著提高了网络估计的精度。我们将提出的模型应用于106名健康受试者的定量肠道微生物组数据，并识别出三个不同的微生物群落，这些微生物群落不是由现有的微生物网络估计模型确定的。我们进一步发现，这些群落是基于微生物利用氧气作为能源的能力而受到歧视的。
Microorganisms play a critical role in host health. The advancement of high-throughput sequencing technology provides opportunities for a deeper understanding of microbial interactions. However, due to the limitations of 16S ribosomal RNA sequencing, microbiome data are zero-inflated, and a quantitative comparison of microbial abundances cannot be made across subjects. By leveraging a recent microbiome profiling technique that quantifies 16S ribosomal RNA microbial counts, we propose a novel Bayesian graphical model that incorporates microorganisms' evolutionary history through a phylogenetic tree prior and explicitly accounts for zero-inflation using the truncated Gaussian copula. Our simulation study reveals that the evolutionary information substantially improves the network estimation accuracy. We apply the proposed model to the quantitative gut microbiome data of 106 healthy subjects, and identify three distinct microbial communities that are not determined by existing microbial network estimation models. We further find that these communities are discriminated based on microorganisms' ability to utilize oxygen as an energy source.