Department of Statistics, School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China.
Department of Electronic Engineering, College of Information Engineering, Shenzhen University, Shenzhen 518060, China.
Bioinformatics. 2018 May 1;34(9):1571-1573. doi: 10.1093/bioinformatics/btx836.
We develop DiffGraph, an R package that integrates four influential differential graphical models for identifying gene network rewiring under two different conditions from gene expression data. The input and output of different models are packaged in the same format, making it convenient for users to compare different models using a wide range of datasets and carry out follow-up analysis. Furthermore, the inferred differential networks can be visualized both non-interactively and interactively. The package is useful for identifying gene network rewiring from input datasets, comparing the predictions of different methods and visualizing the results.
The package is available at https://github.com/Zhangxf-ccnu/DiffGraph.
Supplementary data are available at Bioinformatics online.
我们开发了 DiffGraph 包，这是一个 R 包，集成了四种有影响力的差异图形模型，用于从基因表达数据中识别两种不同条件下的基因网络重布线。不同模型的输入和输出都采用相同的格式进行打包，方便用户使用各种数据集比较不同模型，并进行后续分析。此外，推断出的差异网络可以非交互和交互两种方式进行可视化。该包可用于从输入数据集中识别基因网络重布线，比较不同方法的预测结果，并可视化结果。
该包可在 https://github.com/Zhangxf-ccnu/DiffGraph 上获得。
补充数据可在 Bioinformatics 在线获得。
