Quantification of microbial interactions from 16S rRNA and meta-genomic sequencing data is difficult due to their sparse nature, as well as the fact that the data only provides measures of relative abundance. In this paper, we propose using copula models with mixed zero-beta margins for estimation of taxon-taxon interactions using the normalized microbial relative abundances. Copulas allow for separate modeling of the dependence structure from the margins, marginal covariate adjustment, and uncertainty measurement. Our method shows that a two-stage maximum likelihood approach provides accurate estimation of the model parameters. A corresponding two-stage likelihood-ratio test for the dependence parameter is derived. Simulation studies show that the test is valid and more powerful than tests based upon Pearson's and rank correlations. Furthermore, we demonstrate that our method can be used to build biologically meaningful microbial networks based on the data set of the American Gut Project.
翻译:16S RRNA 和 元基因组测序数据的微生物相互作用的量化由于数据性质稀少,而且数据只提供相对丰度的量度,因此难以对16S RRNA 和 元基因组测序数据的微生物相互作用进行量化。在本文中,我们提议使用混合的零比差的椰子模型来使用正常的微生物相对丰度来估计税税税的相互作用。 Copula可以将依赖性结构与边际、边际共变调整和不确定性测量分别建模。我们的方法表明,两阶段的最大可能性方法可以准确估计模型参数。对依赖性参数进行相应的两阶段概率拉比测试。模拟研究表明,测试是有效的,而且比基于Pearson 和级相关关系进行的测试更有力。此外,我们证明,我们的方法可以用来建立以美国古特项目数据集为基础的具有生物意义的微生物网络。