基于多平台基因表达数据的水稻干旱和盐胁迫相关基因预测

刘亚文, 张红燕, 曹丹, 李兰芝

Prediction of drought and salt stress-related genes in rice based on multi-platform gene expression data

LIU Ya-Wen, ZHANG Hong-Yan, CAO Dan, LI Lan-Zhi

图4 D_rnaseq数据WGCNA网络分析结果 A、B: 分别基于WGCNA-P和WGCNA-MIC方法的基因聚类树和模块划分, Dynamic Tree Cut表示由原始计算划分的模块, Merged dynamic表示合并后的结果; C、D: 分别基于WGCNA-P和WGCNA-MIC方法的各模块的ME和MS值, 红色表示模块与干旱胁迫正相关, 蓝色表示模块与干旱胁迫负相关。

Fig. 4 WGCNA network analysis of D_rnaseq data A, B: the gene clustering tree and module division based on WGCNA-P and WGCNA-MIC methods, respectively; the Dynamic Tree Cut represents the module divided by the original calculation, the Merged dynamic represents the merged result; C, D: the ME and MS values of each module based on WGCNA-P and WGCNA-MIC methods, respectively; red block means that the module is positively correlated with drought stress, and blue indicates that the module is negatively correlated with drought stress.