论文标题
从丰度数据中考虑交互网络推断中缺失的参与者
Accounting for missing actors in interaction network inference from abundance data
论文作者
论文摘要
网络推论旨在揭示与共同观察到的变量相关的依赖性结构。图形模型提供了一个一般框架，以区分边际和条件依赖性。未观察到的变量（缺失的参与者）可能会引起明显的条件依赖性。在计数数据的上下文中，我们引入了泊松对数正态分布与树状图形模型的混合物，以恢复依赖性结构，包括缺失的参与者。我们设计了一种差异算法并评估其在合成数据上的性能。我们证明了我们方法在两个生态数据集上恢复环境驱动因素的能力。可从github.com/rmomal/nestor获得相应的R软件包。
Network inference aims at unraveling 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 datasets. The corresponding R package is available from github.com/Rmomal/nestor.
