Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation.

中文翻译：

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使用具有信息先验的贝叶斯自适应图形套索推断代谢网络

代谢过程对细胞功能和生存至关重要。我们有兴趣根据一组量化的代谢物推断活化小胶质细胞中的代谢网络，小胶质细胞是大脑中负责与神经疾病相关的神经炎症的主要神经免疫细胞。为了实现这一点，我们应用贝叶斯自适应图形套索和包含协变量之间已知关系的信息先验。为了鼓励稀疏性，贝叶斯图形套索在精度矩阵的非对角线项上放置了双指数先验。贝叶斯自适应图形套索允许每个双指数之前具有唯一的收缩参数。这些收缩参数共享一个共同的伽马超先验。我们扩展了这个模型，通过在收缩参数上制定定制的超先验来创建一个信息丰富的先验结构。通过为每个超先验选择参数值，将参考网络中靠近的节点的概率质量移向零，我们鼓励具有已知关系的协变量之间的边。当样本量相对于要估计的参数数量较小时，这种方法可以提高网络推理的可靠性。当应用于激活小胶质细胞的数据时，推断的网络包括已知关系和潜在兴趣的关联，以供进一步调查。我们鼓励具有已知关系的协变量之间的边缘。当样本量相对于要估计的参数数量较小时，这种方法可以提高网络推理的可靠性。当应用于激活小胶质细胞的数据时，推断的网络包括已知关系和潜在兴趣的关联，以供进一步调查。我们鼓励具有已知关系的协变量之间的边缘。当样本量相对于要估计的参数数量较小时，这种方法可以提高网络推理的可靠性。当应用于激活小胶质细胞的数据时，推断的网络包括已知关系和潜在兴趣的关联，以供进一步调查。