从丰度数据推断相互作用网络中缺失参与者的解释
Accounting for missing actors in interaction network inference from abundance data
摘要：
网络推理的目的是解开与联合观察变量相关的依赖结构。图形模型提供了一个通用的框架来区分边际依赖和条件依赖。未观察到的变量（缺失的演员）可能会引起明显的条件dependencies.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形图形模型的混合，以恢复依赖结构，包括缺失的演员。我们设计了一个变分期望最大化算法，并评估其性能的合成数据。我们证明了我们的方法的能力，以恢复两个生态数据集的环境驱动程序。相应的R包可从github.com/Rmomal/nestor获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
