基因相关性+药物敏感性分析①⾼低风险组某基因表达量差异分析
输⼊⽂件symbol.png
输⼊⽂件risk.png
输⼊⽂件risk.png
#Gemcitabine, GNF.2, GSK269962A, GSK.650394, GW.441756, GW843682X, Imatinib, IPA.3,
#JNJ.26854165, JNK.9L, JNK.Inhibitor.VIII, JW.7.52.1, KIN001.135, KU.55933, Lapatinib,
#Lenalidomide, LFM.A13, Metformin, Methotrexate, MG.132, Midostaurin, Mitomycin.C, MK.2206,
#MS.275, Nilotinib, NSC.87877, NU.7441, Nutlin.3a, NVP.BEZ235, NVP.TAE684, Obatoclax.Mesylate,
#OSI.906, PAC.1, Paclitaxel, Parthenolide, Pazopanib, PD.0325901, PD.0332991, PD.173074, PF.02341066,
#PF.4708671, PF.562271, PHA.665752, PLX4720, Pyrimethamine, QS11, Rapamycin, RDEA119, RO.3306,
#Roscovitine, Salubrinal, SB.216763, SB590885, Shikonin, SL.0101.1, Sorafenib, S.steine,
#Sunitinib, Temsirolimus, Thapsigargin, Tipifarnib, TW.37, Vinblastine, Vinorelbine, Vorinostat,
#VX.680, VX.702, WH.4.023, WO2009093972, WZ.1.84, X17.AAG, X681640, XMD8.85, Z.LLNle.CHO, ZM.447439 >药物列表>#
#引⽤包
library(limma)
library(ggpubr)
library(pRRophetic)
library(ggplot2)
set.seed(12345)
expFile="" #表达输⼊⽂件
riskFile="" #风险输⼊⽂件
drug="Rapamycin" #药物名称，需要修改
setwd("E:\\research") #设置⼯作⽬录
#读取表达输⼊⽂件，并对数据进⾏处理
rt = read.table(expFile, header=T, sep="\t", check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
data=avereps(data)
data=data[rowMeans(data)>0.5,]
#删掉正常样品
group=sapply(strsplit(colnames(data),"\\-"), "[", 4)
group=sapply(strsplit(group,""), "[", 1)
group=gsub("2","1",group)
data=data[,group==0]
data=t(data)
rownames(data)=gsub("(.*?)\\-(.*?)\\-(.*?)\\-(.*)", "\\1\\-\\2\\-\\3", rownames(data))
data=avereps(data)
data=t(data)
#预测药物敏感性
senstivity=pRRopheticPredict(data, drug, selection=1)
senstivity=senstivity[senstivity!="NaN"]
#senstivity[senstivity>quantile(senstivity,0.99)]=quantile(senstivity,0.99)
#读取风险输⼊⽂件
risk=read.table(riskFile, header=T, sep="\t", check.names=F, row.names=1)
#风险⽂件和药物敏感性合并
sameSample=intersect(row.names(risk), names(senstivity))
risk=risk[sameSample, "risk",drop=F]
senstivity=senstivity[sameSample]
rt=cbind(risk, senstivity)
#设置⽐较组
rt$risk=factor(rt$risk, levels=c("low", "high"))
type=levels(factor(rt[,"risk"]))
comp=combn(type, 2)
comparisons
my_comparisons=list()
for(i in 1:ncol(comp)){my_comparisons[[i]]<-comp[,i]}
#绘制箱线图
boxplot=ggboxplot(rt, x="risk", y="senstivity", fill="risk",
xlab="Risk",
ylab=paste0(drug, " senstivity (IC50)"),
legend.title="Risk",
palette=c("green", "red")
)+
stat_compare_means(comparisons=my_comparisons)
pdf(file=paste0(drug, ".pdf"), width=5, height=4.5)
print(boxplot)
dev.off()
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