Network inference aims at unravelling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies. In the context of count data, we introduce a mixture of Poisson log-normal distributions with tree-shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological data sets. The corresponding R package is available from github.com/Rmomal/nestor.

中文翻译：

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从丰度数据推断交互网络中缺失的参与者

网络推理旨在解开与联合观察到的变量相关的依赖结构。图形模型提供了一个通用框架来区分边际依赖和条件依赖。未观察到的变量（缺少参与者）可能会导致明显的条件依赖。在计数数据的背景下，我们引入了泊松对数正态分布与树形图形模型的混合，以恢复依赖结构，包括丢失的演员。我们设计了一个变分 EM 算法并评估其在合成数据上的性能。我们展示了我们的方法在两个生态数据集上恢复环境驱动因素的能力。相应的 R 包可从 github.com/Rmomal/nestor 获得。