IEEE/ACM Trans Comput Biol Bioinform. 2022 Jan-Feb;19(1):513-521. doi: 10.1109/TCBB.2020.3002906. Epub 2022 Feb 3.
It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorporate prior information into differential network analysis. In this study, we propose a new differential network analysis method to address the above challenges. Instead of using the precision matrices, we define the differential networks as the difference of partial correlations, which can exclude the spurious differential edges due to the variants of conditional variances. Furthermore, prior information from multiple hypothesis testing is incorporated using a weighted fused penalty. Simulation studies show that our method outperforms the competing methods. We also apply our method to identify the differential network between luminal A and basal-like subtypes of breast cancers and the differential network between acute myeloid leukemia tumors and normal samples. The hub genes in the differential networks identified by our method carry out important biological functions.
学习基因调控网络在不同条件下如何变化是一项重要任务。已经提出了几种基于高斯图形模型的方法，通过从基因表达数据中推断差异网络来处理这个任务。然而，大多数现有方法将差异网络定义为精度矩阵的差异，这可能包括由于条件方差变化引起的虚假差异边缘。此外，关于特定条件网络和差异网络的先验信息可以从其他领域获得。将先验信息纳入差异网络分析是有用的。在这项研究中，我们提出了一种新的差异网络分析方法来解决上述挑战。我们不是使用精度矩阵，而是将差异网络定义为偏相关的差异，这可以排除由于条件方差变化引起的虚假差异边缘。此外，使用加权融合惩罚将来自多个假设检验的先验信息纳入其中。模拟研究表明，我们的方法优于竞争方法。我们还应用我们的方法来识别乳腺导管癌的腔 A 型和基底样亚型之间的差异网络以及急性髓系白血病肿瘤和正常样本之间的差异网络。我们方法识别的差异网络中的枢纽基因执行重要的生物学功能。
