Wen Tao, Xie Penghao, Yang Shengdie, Niu Guoqing, Liu Xiaoyu, Ding Zhexu, Xue Chao, Liu Yong-Xin, Shen Qirong, Yuan Jun
Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Key Laboratory of Green Intelligent Fertilizer Innovation, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers Nanjing Agricultural University Nanjing China.
Nanjing Meta Biotechnology Co., Ltd. Nanjing China.
Imeta. 2022 Jun 13;1(3):e32. doi: 10.1002/imt2.32. eCollection 2022 Sep.
The network analysis has attracted increasing attention and interest from ecological academics, thus it is of great necessity to develop more convenient and powerful tools. For that reason, we have developed an R package, named "ggClusterNet," to complete and display the network analysis in an easier manner. In that package, ten network layout algorithms are designed to better display the modules of microbiome network (randomClusterG, PolygonClusterG, PolygonRrClusterG, ArtifCluster, randSNEClusterG, PolygonModsquareG, PolyRdmNotdCirG, model_Gephi.2, model_igraph, and model_maptree). For the convenience of the users, many functions related to microbial network analysis, such as corMicor(), net_properties(), node_properties(), ZiPiPlot(), random_Net_compate(), are integrated to complete the network mining. Furthermore, the pipeline function named network.2() and corBionetwork() are also added for the quick achievement of the network or bipartite network analysis as well as their in-depth mining. The ggClusterNet is publicly available via GitHub (https://github.com/taowenmicro/ggClusterNet/) or Gitee (https://gitee.com/wentaomicro/ggClusterNet) for users' access. A complete description of the usages can be found on the manuscript's GitHub page (https://github.com/taowenmicro/ggClusterNet/wiki).
网络分析已引起生态学界越来越多的关注和兴趣，因此开发更便捷、功能更强大的工具非常必要。出于这个原因，我们开发了一个名为“ggClusterNet”的R包，以便更轻松地完成和展示网络分析。在该包中，设计了十种网络布局算法，以更好地展示微生物群落网络的模块（randomClusterG、PolygonClusterG、PolygonRrClusterG、ArtifCluster、randSNEClusterG、PolygonModsquareG、PolyRdmNotdCirG、model_Gephi.2、model_igraph和model_maptree）。为方便用户，还集成了许多与微生物网络分析相关的函数，如corMicor()、net_properties()、node_properties()、ZiPiPlot()、random_Net_compate()等，以完成网络挖掘。此外，还添加了名为network.2()和corBionetwork()的管道函数，以快速实现网络或二分网络分析及其深入挖掘。ggClusterNet可通过GitHub（https://github.com/taowenmicro/ggClusterNet/）或Gitee（https://gitee.com/wentaomicro/ggClusterNet）公开获取，供用户使用。有关用法的完整描述可在该稿件的GitHub页面（https://github.com/taowenmicro/ggClusterNet/wiki）上找到。
