经典信号通路作图工具包pathview

介绍个数据整合和可视化的R包 pathview。

首先安装包并载入数据：

source("http://bioconductor.org/biocLite.R")
biocLite("pathview") 
library(pathview) 
# 载入数据
data(gse16873.d) 
data(demo.paths) 

基因表达变化数据框如下所示，行是基因ID，列是样本ID，变化范围是-1到1.

对单样本做经典星号通路可视化，"Cell Cycle"通过 gene.data和 pathway.id指定，表达谱文件是人类的，所以 species="hsa"，

pv.out  pathview(gene.data = gse16873.d[, 1], pathway.id = demo.paths$sel.paths[1], species = "hsa", out.suffix = "gse16873", kegg.native = T) 




    

具体查看图里每个节点的数据，每个节点的kegg名和ID都如下表列出:

head(pv.out$plot.data.gene) 

如果想删除与自己数据里无关的节点：

pv.out  pathview(gene.data = gse16873.d[, 1], pathway.id = demo.paths$sel.paths[1], species = "hsa", out.suffix = "gse16873.2layer", kegg.native = F, sign.pos = demo.paths$spos[1], same.layer = F)




    

将组合在一起的接节点画分开画：

pv.out  pathview(gene.data = gse16873.d[, 1], pathway.id = demo.paths$sel.paths[1], species = "hsa", out.suffix = "gse16873.split", kegg.native = F, sign.pos = demo.paths$spos[i], split.group = T) 

完整画出所有节点之间的关系，包括间接联系：

pv.out  pathview(gene.data = gse16873.d[, 1], pathway.id = demo.paths$sel.paths[1],  species = "hsa", out.suffix = "gse16873.split.expanded", kegg.native = F,  sign.pos = demo.paths$spos[i], split.group = T, expand.node = T) 

还可以将基因数据和化合物数据与代谢途径整合可视化，包括小分子、代谢物、酶等数据以及多样本作图均可以使用这个包。

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