16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss .

中文翻译：

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通过 codaloss 对成分数据进行直接交互网络推理

16S rRNA基因测序和全微生物组测序使定量分析微生物群落组成以及微生物群落、微生物和宿主之间的关系成为可能且稳定。分析微生物组组成数据的一个重要步骤是推断微生物物种之间的直接相互作用网络，揭示调节其群落相互作用的潜在潜在机制。然而，由于微生物组数据的组成性质，标准的统计分析可能会得到虚假的结果；因此，微生物群落的网络恢复仍然具有挑战性。在这里，我们提出了一种新的损失函数，称为 codaloss，用于在稀疏假设下进行直接微生物相互作用网络估计。我们开发了一种交替方向优化算法，以获得作为估计量的 codaloss 的稀疏解。与其他最先进的方法相比，我们的模型对微生物网络的假设较少。模拟和真实微生物组数据结果表明，我们的方法在网络推理方面优于其他方法。codaloss 的实现可从 https://github.com/xuebaliang/Codaloss 获得。