如何用Pathview画出高大上的基因与代谢通路热图？

Pathview是一个可视化KEGG通路的R包，它会在线从KEGG网站上下载KEGG通路，并进行个性化处理，例如填上不同的颜色。 今天就来分享一下如何用Pathview画出高大上的基因与代谢通路热图。 基因热通路图

在此之前现在R中把必要的包安装好，并加载上

if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install(c("Rgraphviz", "png", "KEGGgraph", "org.Hs.eg.db","pathview","gage")) library(pathview) 首先，我们把数据准备好，第一列为基因的ENTREZ号，第二列为基因的变化倍数

把数据导入R中data<-read.csv("data1.xls",row.names=1,sep="\t",head=T)

head(data,10)

开始进行绘制图片，gene.data是用于绘图的数据，pathway.id是需要绘制的KEGG通路号，species是物种的简称，out.suffix是导出的数据后缀pv.out <- pathview(gene.data = data, pathway.id = "04110",species = "hsa", out.suffix = "data1")

运行完成后，可以看到文件夹下出现了三个文件，其中带data1的是绘制完的图片，其他是用于绘图的KEGG原始数据。查看图片结果，可以看到基因的变化已经用热图的形式展现在通路上了分为两个图层，图像的立体感增加了pv.out <- pathview(gene.data = data, pathway.id = demo.paths$sel.paths[i],

species = "hsa", out.suffix = "data1", kegg.native = T,

same.layer = F)

查看物种所有通路 paths.hsa

也可以批量对几条通路同时进行上色，先定义几条通路得到了所有选择的通路结果可以直接导出pdf格式，先定义图例位置pv.out <- pathview(gene.data = data, pathway.id = mypath,

species = "hsa", out.suffix = "data3", kegg.native = F,

sign.pos = sign)定义图例位置

不同反应分开画 pv.out <- pathview(gene.data = data, pathway.id = demo.paths$sel.paths[i], species = "hsa", out.suffix = "data4", kegg.native = F, sign.pos = demo.paths$spos[i], split.group = T)

线段拓宽pv.out <- pathview(gene.data = data, pathway.id = demo.paths$sel.paths[i],

species = "hsa", out.suffix = "data5", kegg.native = F,

sign.pos = demo.paths$spos[i], split.group = T, expand.node = T)

代谢物热通路图

下面开始画代谢物的通路热图，第一列是化合物的CPD号，第二列是变化倍数 导入数据，并开始绘制 data2<-read.csv("data2.xls",row.names=1,sep="\t",head=T)

pv.out <- pathview(cpd.data = data2,

pathway.id = demo.paths$sel.paths[i], species = "hsa", out.suffix = "data6",

keys.align = "y", kegg.native = T, key.pos = demo.paths$kpos1[i])

同时绘制基因与代谢物通路热图 pv.out <- pathview(gene.data = data, cpd.data = data2, pathway.id = demo.paths$sel.paths[i], species = "hsa", out.suffix = "data7", keys.align = "y", kegg.native = F, key.pos = demo.paths$kpos2[i], sign.pos = demo.paths$spos[i], cpd.lab.offset = demo.paths$offs[i])

展现不同数据中同一条通路的代谢物变化，先准备数据，并导入

pv.out <- pathview(gene.data = gse16873.d[, 1:3],

cpd.data = data3, pathway.id = demo.paths$sel.paths[i],

species = "hsa", out.suffix = "data8", keys.align = "y",

kegg.native = T, match.data = F, multi.state = T, same.layer = T)

导出pdf结果 pv.out <- pathview(gene.data = gse16873.d[, 1:3], cpd.data = sim.cpd.data2[, 1:2], pathway.id = demo.paths$sel.paths[i], species = "hsa", out.suffix = "data9", keys.align = "y", kegg.native = F, match.data = F, multi.state = T, same.layer = T, key.pos = demo.paths$kpos2[i], sign.pos = demo.paths$spos[i])

准备多组数据并导入进行t检验data.t <- apply(data, 1, function(x) t.test(x,

alternative = "two.sided")$p.value)

根据基因的t检验结果和代谢物的差异倍数进行筛选

sel.genes <- names(data.t)[data.t < 0.1]

sel.cpds <- names(sim.cpd.data)[abs(sim.cpd.data) > 0.5]

得到挑选的基因和代谢物的结果 pv.out <- pathview(gene.data = sel.genes, cpd.data = sel.cpds, pathway.id = demo.paths$sel.paths[i], species = "hsa", out.suffix = "sel.genes.sel.cpd", keys.align = "y", kegg.native = T, key.pos = demo.paths$kpos1[i], limit = list(gene = 5, cpd = 2), bins = list(gene = 5, cpd = 2), na.col = "gray", discrete = list(gene = T, cpd = T)) pv.out <- pathview(gene.data = sel.genes, cpd.data = sim.cpd.data, pathway.id = demo.paths$sel.paths[i], species = "hsa", out.suffix = "sel.genes.cpd", keys.align = "y", kegg.native = T, key.pos = demo.paths$kpos1[i], limit = list(gene = 5, cpd = 1), bins = list(gene = 5, cpd = 10), na.col = "gray", discrete = list(gene = T, cpd = F))

使用不同的ID格式 pv.out <- pathview(gene.data = gene.ensprot, cpd.data = cpd.cas, gene.idtype = gene.idtype.list[4], cpd.idtype = cpd.simtypes[2], pathway.id = demo.paths$sel.paths[i], species = "hsa", same.layer = T, out.suffix = "gene.ensprot.cpd.cas", keys.align = "y", kegg.native = T, key.pos = demo.paths$kpos2[i], sign.pos = demo.paths$spos[i], limit = list(gene = 3, cpd = 3), bins = list(gene = 6, cpd = 6))

进行ID转换 id.map.cas <- cpdidmap(in.ids = names(cpd.cas), in.type = cpd.simtypes[2], out.type = "KEGG COMPOUND accession") cpd.kc <- mol.sum(mol.data = cpd.cas, id.map = id.map.cas) id.map.ensprot <- id2eg(ids = names(gene.ensprot), category = gene.idtype.list[4], org = "Hs") gene.entrez <- mol.sum(mol.data = gene.ensprot, id.map = id.map.ensprot) eco.dat.kegg <- sim.mol.data(mol.type="gene",id.type="kegg",species="eco",nmol=3000) eco.dat.entrez <- sim.mol.data(mol.type="gene",id.type="entrez",species="eco",nmol=3000) 定义条件进行统计 data(korg) head(korg) sum(korg[,"entrez.gnodes"]=="1",na.rm=T) sum(korg[,"entrez.gnodes"]=="0",na.rm=T) na.idx=is.na(korg[,"ncbi.geneid"]) sum(na.idx)

#画出人工绘制的通路图ko.data=sim.mol.data(mol.type="gene.ko", nmol=5000)

pv.out <- pathview(gene.data = ko.data, pathway.id = "04112",species = "ko", out.suffix = "ko.data", kegg.native = T)

好了，以上就是使用Pathview画出高大上的基因与代谢通路热图的操作，希望对你有所帮助。如果对Pathview的使用有什么问题可以评论区留言，更多你不知道的生物学小工具教程及下载欢迎继续关注~