以KEGG富集结果为例
首先进行KEGG富集分析
library(clusterProfiler)
de_ekp <- enricher(gene,
TERM2GENE = pathway2gene,
TERM2NAME = pathway2name,
pvalueCutoff = 1,
qvalueCutoff = 1)
1、展示前5个通路
cnetplot(de_ekp,
foldChange =foldchange,
showCategory = 5,
node_label = "all", # category | gene | all | none
colorEdge = TRUE)
foldchange如何获得呢
x数据
在这里插入图片描述
foldchange = x$log2Fold_change
names(foldchange) <- x$symbol
foldchange
2、展示特定的通路
y <- c("Fatty acid biosynthesis","Fatty acid metabolism","PPAR signaling pathway","Insulin signaling pathway","Fatty acid degradation","Glucagon signaling pathway","Insulin resistance")
# 把想要展示的通路名称赋值给y#
cnetplot(de_ekp,
foldChange =foldchange,
showCategory = y,
node_label = "all", # category | gene | all | none
colorEdge = TRUE)
更改颜色
p1 <- cnetplot(de_ekp,
foldChange =log2(foldchange),
showCategory = y,
node_label = "all", # category | gene | all | none
##circular = TRUE,#
colorEdge = TRUE)
p1+scale_color_gradientn(colours = c("green","blue", "red"))
更改颜色资料:
https://support.bioconductor.org/p/p133748/