用于评估蛋白质组网络中肿瘤异质性的稳健贝叶斯图回归模型
Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks
AI总结：
本文提出稳健贝叶斯图回归模型rBGR，通过随机边际变换和图回归技术处理非正态数据下的异质网络估计，并在肺癌与卵巢癌蛋白质组网络分析中揭示了受免疫细胞丰度差异性影响的关键蛋白质相互作用。
摘要：
图模型是探究高通量数据中复杂依赖结构的有力工具。然而，现有的大多数图模型都采用两种经典假设之一：（i）同质图，即所有个体共享一个共同网络；或（ii）正态性假设，尤其是在高斯图模型的背景下。这两种假设都具有限制性，在某些应用中可能不成立，例如癌症中的蛋白质组网络。为此，我们提出了一种称为稳健贝叶斯图回归（rBGR）的方法，用于估计非正态分布数据的异质图。rBGR是一个灵活的框架，通过随机边际变换适应非正态性，并通过图回归技术构建协变量依赖的图以适应异质性。我们在此类模型中提出了一种新的边依赖刻画，称为带协变量的条件符号独立性，并配套提出了一种高效的后验采样算法。在模拟研究中，我们证明，对于在不同非正态性水平下生成的数据，rBGR在边选择和协变量选择方面均优于现有的图回归模型。我们使用rBGR评估两种癌症的蛋白质组网络：肺癌和卵巢癌，以系统研究肿瘤内免疫原性异质性的影响。我们的分析揭示了若干重要的蛋白质-蛋白质相互作用，这些相互作用受到免疫细胞丰度的差异性影响；其中一些结果印证了现有的生物学知识，另一些则是新的发现。
英文摘要：
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical assumptions: (i) a homogeneous graph with a common network for all subjects; or (ii) an assumption of normality especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer. To this end, we propose an approach termed robust Bayesian graphical regression (rBGR) to estimate heterogeneous graphs for non-normally distributed data. rBGR is a flexible framework that accommodates non-normality through random marginal transformations and constructs covariate-dependent graphs to accommodate heterogeneity through graphical regression techniques. We formulate a new characterization of edge dependencies in such models called conditional sign independence with covariates along with an efficient posterior sampling algorithm. In simulation studies, we demonstrate that rBGR outperforms existing graphical regression models for data generated under various levels of non-normality in both edge and covariate selection. We use rBGR to assess proteomic networks across two cancers: lung and ovarian, to systematically investigate the effects of immunogenic heterogeneity within tumors. Our analyses reveal several important protein-protein interactions that are differentially impacted by the immune cell abundance; some corroborate existing biological knowledge whereas others are novel findings.
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