Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.

中文翻译：

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多层基因组网络中的贝叶斯结构学习

来自多个基因组平台的数据的综合网络建模提供了对交互系统整体图的洞察，以及跨许多疾病领域（包括癌症）的信息流。基本数据结构由针对每个个体的一系列按层次排序的数据集组成，这有助于整合不同的输入，例如基因组、转录组和蛋白质组数据。在这种情况下，一个主要的分析任务是对网络的分层架构进行建模，其中顶点可以自然地划分为有序层，由多个平台决定，并表现出无向和有向关系。我们提出了一个多层高斯图形模型（mlGGM）来研究人类癌症中这种多级基因组网络中的条件独立结构。我们实现了基于变量选择技术的贝叶斯节点明智选择 (BANS) 方法，该方法连贯地解释了 mlGGM 中多种类型的依赖关系；这种灵活的策略利用特定于边缘的先验知识并选择稀疏和可解释的模型。通过在各种场景下生成的模拟数据，我们证明 BANS 优于其他现有的基于多元回归的方法。我们对多种癌症类型关键信号通路的整合基因组网络分析突出了 p53 整合网络的共性和差异以及 BRCA2 对 p53 的表观遗传效应及其与 T68 磷酸化 CHK2 的相互作用，这可能具有寻找生物标志物和治疗靶点的转化效用。