Bayesian inference of networks across multiple sample groups and data types
Bayesian inference of networks across multiple sample groups and data types
复制标题
跨多个样本组和数据类型的网络贝叶斯推理
DOI：
10.1093/biostatistics/kxy078
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发表时间：
2018
期刊：
影响因子：
2.1
通讯作者：
Vannucci, Marina
中科院分区：
文献类型：
--
作者：
Shaddox, Elin;Peterson, Christine B;Stingo, Francesco C;Hanania, Nicola A;Cruickshank-Quinn, Charmion;Kechris, Katerina;Bowler, Russell;Vannucci, Marina
在本文中，我们开发了一个图形建模框架，用于跨多个样本组和数据类型的网络推理。在医学研究中，当一组由于疾病分期或亚型不同而可能具有异质性的受试者在多个平台（如代谢组学、蛋白质组学或转录组学数据）上进行分析时，就会出现这种情况。我们提出的贝叶斯层次模型首先使用马尔可夫随机场将每个平台内的网络结构连接起来，然后在样本组之间关联边缘选择，然后将网络相似性参数跨平台连接起来。这使我们能够以灵活的方式进行联合估计，因为我们不假设跨数据类型的影响的方向性，也不假设跨样本组和平台的网络相似性程度。此外，我们的模型公式允许不同数据类型的变量数量和主题数量不同，并且只要求我们拥有相同组集的数据。我们通过模拟研究和对慢性阻塞性肺疾病不同严重程度受试者的基因表达水平和代谢物丰度的应用来说明所提出的方法。贝叶斯推理;慢性阻塞性肺病（COPD）；数据集成;高斯图形模型；马尔可夫随机场先验；前面是长钉和厚板。
In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of chronic obstructive pulmonary disease. Bayesian inference; Chronic obstructive pulmonary disease (COPD); Data integration; Gaussian graphical model; Markov random field prior; Spike and slab prior.