Department of Mathematical Sciences, University of Arkansas, Arkansas, AR 72701, USA.
Genes (Basel). 2020 Feb 5;11(2):167. doi: 10.3390/genes11020167.
The nonparanormal graphical model has emerged as an important tool for modeling dependency structure between variables because it is flexible to non-Gaussian data while maintaining the good interpretability and computational convenience of Gaussian graphical models. In this paper, we consider the problem of detecting differential substructure between two nonparanormal graphical models with false discovery rate control. We construct a new statistic based on a truncated estimator of the unknown transformation functions, together with a bias-corrected sample covariance. Furthermore, we show that the new test statistic converges to the same distribution as its oracle counterpart does. Both synthetic data and real cancer genomic data are used to illustrate the promise of the new method. Our proposed testing framework is simple and scalable, facilitating its applications to large-scale data. The computational pipeline has been implemented in the R package , which is freely available through the Comprehensive R Archive Network.
非参数图形模型已成为建模变量之间依赖结构的重要工具，因为它可以灵活处理非高斯数据，同时保持高斯图形模型的良好可解释性和计算便利性。在本文中，我们考虑了在控制误报率的情况下检测两个非参数图形模型之间差异子结构的问题。我们基于未知变换函数的截断估计量以及偏差校正的样本协方差构建了一个新的统计量。此外，我们证明了新的检验统计量的收敛分布与它的理想对应量相同。合成数据和真实的癌症基因组数据都被用来说明新方法的前景。我们提出的测试框架简单且可扩展，方便其应用于大规模数据。该计算流程已在 R 包 中实现，可通过 Comprehensive R Archive Network 免费获取。
