差异分析完以后，就有了基因列表和差异倍数，有了这两个东西，就可以用clusterprofiler做GSEA，然后有了pathview包的话，就可以可视化KEGG通路
- 怎么做GSEA，可以参考我之前的文章，R做GSEA富集分析
- 基因列表必须是
entrez id，也就是数字，这个可以用clusterprofiler转换
geneList<-gene $logFC #可以是folodchange，也可以是logFC或P值
names(geneList)=gene $ENTREZID #使用转换好的ID
geneList=sort(geneList,decreasing = T)
有了geneList，然后就可以做GSEA，也可以直接KEGG可视化，但是要知道你需要的通路id，比如Cell Cycle是‘hsa04110’
library(pathview)
- 我们可以看看可视化的教程是怎么样的
pathview(gene.data = NULL, cpd.data = NULL, pathway.id,
species = "hsa", kegg.dir = ".", cpd.idtype = "kegg", gene.idtype =
"entrez", gene.annotpkg = NULL, min.nnodes = 3, kegg.native = TRUE,
map.null = TRUE, expand.node = FALSE, split.group = FALSE, map.symbol =
TRUE, map.cpdname = TRUE, node.sum = "sum", discrete=list(gene=FALSE,
cpd=FALSE), limit = list(gene = 1, cpd = 1), bins = list(gene = 10, cpd
= 10), both.dirs = list(gene = T, cpd = T), trans.fun = list(gene =
NULL, cpd = NULL), low = list(gene = "green", cpd = "blue"), mid =
list(gene = "gray", cpd = "gray"), high = list(gene = "red", cpd =
"yellow"), na.col = "transparent", ...)
这里面内容很多，就举几个简单的代码
pathview(geneList,pathway.id='hsa04110')
运行完这个代码后在R-studio里其实是看不到图的，因为图直接加载到目标文件夹里了，如果不知道是哪个文件夹，就输入这个
getwd()
然后就会在这个文件夹里看到三个文件，一个是hsa04110.xml、一个是hsa04110.png，一个是hsa04110.pathview.png，而我们需要的就是这个hsa04110.pathview.png,长下面这个样子，有红有绿，其实就是代表了红色是上调，绿色是下调。
-
然而，这个图太没有颜值，我们就可以定制一下，就是从低到高的配色
pathview(geneList,pathway.id='hsa04110',low = list(gene = "#6D9EC1", cpd = "#6D9EC1"), mid =
list(gene = "gray", cpd = "gray"), high = list(gene = "#E46726", cpd =
"#E46726"))
再次回到之前的文件夹，会发现hsa04110.pathview.png已经变样了，由于图片是直接覆盖的，如果需要之前的图，把之前的图重命名即可。
- 然后，我们还可以把原来的KEGG基因标签（或EC编号）替换为官方基因符号，只要加一句
same.layer = F
pathview(geneList,pathway.id='hsa04110',low = list(gene = "#6D9EC1", cpd = "#6D9EC1"), mid =
list(gene = "gray", cpd = "gray"), high = list(gene = "#E46726", cpd =
"#E46726"),same.layer = F)
看出来不同了吗，就是格格里面的符号变了，也更清晰了，其实就是加了个图层
- 然而，导出的图是png，有时候再次处理，还可以变成PDF，只要加一句
kegg.native = F，这样就有了一个pdf的图
pathview(geneList,pathway.id='hsa04110',low = list(gene = "#6D9EC1", cpd = "#6D9EC1"), mid =
list(gene = "gray", cpd = "gray"), high = list(gene = "#E46726", cpd =
"#E46726"),same.layer = F,kegg.native = F)
后面的定制，自己可以慢慢DIY