Learning Microbial Interaction Networks from Metagenomic Count Data
Learning Microbial Interaction Networks from Metagenomic Count Data
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DOI：
10.1089/cmb.2016.0061
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发表时间：
2016-06-01
影响因子：
1.7
通讯作者：
Jojic, Vladimir
中科院分区：
文献类型：
--
作者：
Biswas, Surojit;Mcdonald, Meredith;Jojic, Vladimir
许多微生物与高等真核生物相关联并影响其生命力。要为宿主的利益设计微生物组，我们必须了解群落组装和维护的规则，这在很大程度上需要了解群落成员之间的直接相互作用。为此，我们开发了一个泊松多变量正态分层模型，从标准宏基因组测序实验的基于计数的输出中学习直接相互作用。我们的模型在泊松层控制混杂预测因子，并使用l（1）惩罚精度矩阵在多元正态层捕获直接的分类群-分类群相互作用。我们在合成实验中表明，我们的方法轻松优于最先进的方法，如SparCC和图形套索（glasso）。在一个真实的在植物扰动实验的9名成员的细菌群落，我们表明我们的模型，但不是SparCC或glasso，正确地解决了直接的相互作用结构之间的三个社区成员，与拟南芥根。我们的结论是，我们的方法提供了一个结构化的，准确的，分布合理的方式建模相关的计数为基础的随机变量和捕捉它们之间的直接相互作用。
Many microbes associate with higher eukaryotes and impact their vitality. To engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenance that, in large part, demand an understanding of the direct interactions among community members. Toward this end, we have developed a Poisson-multivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer and captures direct taxon-taxon interactions at the multivariate normal layer using an l(1) penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real in planta perturbation experiment of a nine-member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among three community members that associates with Arabidopsis thaliana roots. We conclude that our method provides a structured, accurate, and distributionally reasonable way of modeling correlated count-based random variables and capturing direct interactions among them.