论文标题
对高维网络的差异分析的协变量调整的推断
Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks
论文作者
论文摘要
与疾病状况相对应的生物网络之间的差异可以帮助描述潜在的疾病机制。现有的差异网络分析方法不能解释网络对协变量的依赖性。结果，这些方法可能检测到协变量对疾病状况和网络的影响引起的杂差连接。为了解决这个问题，我们提出了一项一般协变量调整的测试，以进行差异网络分析。我们的方法通过测试无效的假设，即对于具有相同协变量并且疾病状况不同的个体而言，网络相同。我们在一项模拟研究中表明，与不考虑协变量的幼稚假设测试程序相比，协变量调整后的测试对I型误差进行了改善。我们还表明，在某些设置中，我们提出的方法提供了改进的检测差异连接的功能。我们通过应用亚型来检测乳腺癌基因共表达网络的差异来说明我们的方法。
Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result, these approaches may detect spurious differential connections induced by the effect of the covariates on both the disease condition and the network. To address this issue, we propose a general covariate-adjusted test for differential network analysis. Our method assesses differential network connectivity by testing the null hypothesis that the network is the same for individuals who have identical covariates and only differ in disease status. We show empirically in a simulation study that the covariate-adjusted test exhibits improved type-I error control compared with naïve hypothesis testing procedures that do not account for covariates. We additionally show that there are settings in which our proposed methodology provides improved power to detect differential connections. We illustrate our method by applying it to detect differences in breast cancer gene co-expression networks by subtype.
