Department of Statistics, School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China.
Department of Electronic Engineering, Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, China.
Bioinformatics. 2019 Sep 1;35(17):3184-3186. doi: 10.1093/bioinformatics/btz051.
To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks.
R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR.
Supplementary data are available at Bioinformatics online.
为了识别不同条件下的生物网络重连，我们开发了一个用户友好的 R 包，名为 DiffNetFDR，用于实现两种用于测试不同高斯图形模型差异的方法。与现有工具相比，我们的方法具有以下特点：（i）它们基于高斯图形模型，可以捕捉条件依赖性的变化；（ii）它们以数据驱动的方式确定调整参数；（iii）它们采用多重检验程序来控制总体假发现率；（iv）我们的方法基于偏相关系数定义差异网络，从而可以排除由条件方差变化引起的虚假差异边缘。我们还开发了一个 Shiny 应用程序，提供更简单的分析和可视化。进行了模拟研究来评估我们方法的性能。我们还将我们的方法应用于两个真实的基因表达数据集。通过识别的差异网络的生物学意义验证了我们方法的有效性。
R 包和 Shiny 应用程序可在 https://github.com/Zhangxf-ccnu/DiffNetFDR 上获得。
补充数据可在生物信息学在线获得。
