#引用包
library(GSVA)
library(limma)
library(GSEABase)
expFile="symbol-L.txt"    
gmtFile="immune.gmt"    
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))
mat=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
mat=avereps(mat)
mat=mat[rowMeans(mat)>0,]
geneSet=getGmt(gmtFile, geneIdType=SymbolIdentifier())
ssgseaScore=gsva(mat, geneSet, method='ssgsea', kcdf='Gaussian', abs.ranking=TRUE)
normalize=function(x){
  return((x-min(x))/(max(x)-min(x)))}
ssgseaOut=normalize(ssgseaScore)
ssgseaOut=rbind(id=colnames(ssgseaOut),ssgseaOut)
write.table(ssgseaOut, file="ssgseaOut-L.txt", sep="\t", quote=F, col.names=F)
library(utils)
library(limma)
library(estimate)
inputFile="symbol-L-yin.txt"       #表达输入文件
rt=read.table(inputFile, 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)
out=data[rowMeans(data)>0,]
out=rbind(ID=colnames(out),out)
write.table(out,file="uniq.symbol-L-yin.txt",sep="\t",quote=F,col.names=F)
filterCommonGenes(input.f="uniq.symbol-L-yin.txt", 
                  output.f="commonGenes.gct", 
                  id="GeneSymbol")
estimateScore(input.ds = "commonGenes.gct",
              output.ds="estimateScore.gct")
scores=read.table("estimateScore.gct", skip=2, header=T)
rownames(scores)=scores[,1]
scores=t(scores[,3:ncol(scores)])
rownames(scores)=gsub("\\.", "\\-", rownames(scores))
out=rbind(ID=colnames(scores), scores)
write.table(out, file="TMEscores-L-yin.txt", sep="\t", quote=F, col.names=F)
library(pheatmap)                
ssgseaFile="ssgseaOut-L-all.txt"      
clusterFile="cluster-L.txt"       
estimateFile="TMEscore-L.txt"     
Type=read.table(clusterFile, header=F, sep="\t", check.names=F, row.names=1)
colnames(Type)=c("Subtype")
Type=Type[order(Type[,"Subtype"],decreasing=T),,drop=F]
Type$Subtype=factor(Type$Subtype, levels=unique(Type$Subtype))
rt=read.table(ssgseaFile, header=T, sep="\t", check.names=F, row.names=1)
rt=rt[,row.names(Type)]
score=read.table(estimateFile, header=T, sep="\t", check.names=F, row.names=1)
score=score[row.names(Type),,drop=F]
cluster=cbind(Type, score)
ann_colors=list()
clusterCol=c("blue", "red")
names(clusterCol)=levels(factor(Type$Subtype))
ann_colors[["Subtype"]]=clusterCol
pdf("estimateHM-L.pdf", width=9, height=5)
pheatmap(rt, annotation=cluster,
         annotation_colors = ann_colors,
         color = colorRampPalette(c(rep("blue",3), "white", rep("red",3)))(50),
         cluster_cols =F,
         scale="row",
         show_colnames=F,
         fontsize=8,
         fontsize_row=8,
         fontsize_col=3)
dev.off()
library(reshape2)
library(ggpubr)
clusterFile="cluster-L.txt"          
estimateFile="TMEscore-L.txt"      
Type=read.table(clusterFile, header=F, sep="\t", check.names=F, row.names=1)
colnames(Type)=c("Subtype")
Type=Type[order(Type[,"Subtype"],decreasing=T),,drop=F]
Type$Subtype=factor(Type$Subtype, levels=unique(Type$Subtype))
score=read.table(estimateFile, header=T, sep="\t", check.names=F, row.names=1)
score=score[,1:3]
score=score[row.names(Type),,drop=F]
rt=cbind(Type, score)
data=melt(rt, id.vars=c("Subtype"))
colnames(data)=c("Subtype", "scoreType", "Score")
p=ggviolin(data, x="scoreType", y="Score", fill = "Subtype",
           xlab="",
           ylab="TME score",
           legend.title="Subtype",
           add = "boxplot", add.params = list(color="white"),
           palette = c("blue","red"), width=1)
p=p+rotate_x_text(45)
p1=p+stat_compare_means(aes(group=Subtype),
                        method="wilcox.test",
                        symnum.args=list(cutpoints = c(0, 0.001, 0.01, 0.05, 1), symbols = c("***", "**", "*", " ")),
                        label = "p.signif")
pdf(file="vioplot.pdf", width=6, height=5)
print(p1)
dev.off()
library(limma)
inputFile="symbol-L.txt"                                             
fdrFilter=0.05                                                   
logFCfilter=1                                                    
conNum=4                                                        
treatNum=5                                                      
outTab=data.frame()
grade=c(rep(1,treatNum),rep(2,conNum))
rt=read.table(inputFile,sep="\t",header=T,check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
data=avereps(data)
data=data[rowMeans(data)>0.2,]
for(i in row.names(data)){
  geneName=unlist(strsplit(i,"\\|",))[1]
  geneName=gsub("\\/", "_", geneName)
  rt=rbind(expression=data[i,],grade=grade)
  rt=as.matrix(t(rt))
  wilcoxTest<-wilcox.test(expression ~ grade, data=rt)
  conGeneMeans=mean(data[i,1:conNum])
  treatGeneMeans=mean(data[i,(conNum+1):ncol(data)])
  logFC=log2(treatGeneMeans)-log2(conGeneMeans)
  pvalue=wilcoxTest$p.value
  conMed=median(data[i,1:conNum])
  treatMed=median(data[i,(conNum+1):ncol(data)])
  diffMed=treatMed-conMed
  if( ((logFC>0) & (diffMed>0)) | ((logFC<0) & (diffMed<0)) ){  
    outTab=rbind(outTab,cbind(gene=i,conMean=conGeneMeans,treatMean=treatGeneMeans,logFC=logFC,pValue=pvalue))
  }
}
pValue=outTab[,"pValue"]
fdr=p.adjust(as.numeric(as.vector(pValue)),method="fdr")
outTab=cbind(outTab,fdr=fdr)
write.table(outTab,file="all.txt",sep="\t",row.names=F,quote=F)
outDiff=outTab[( abs(as.numeric(as.vector(outTab$logFC)))>logFCfilter & as.numeric(as.vector(outTab$fdr))<fdrFilter),]
write.table(outDiff,file="diff.xls",sep="\t",row.names=F,quote=F)
heatmap=rbind(ID=colnames(data[as.vector(outDiff[,1]),]),data[as.vector(outDiff[,1]),])
write.table(heatmap,file="diffGeneExp.txt",sep="\t",col.names=F,quote=F)
library("org.Hs.eg.db")         
rt=read.table("gene.txt",sep="\t",check.names=F,header=T)     
genes=as.vector(rt[,1])
entrezIDs <- mget(genes, org.Hs.egSYMBOL2EG, ifnotfound=NA)       
entrezIDs <- as.character(entrezIDs)
out=cbind(rt,entrezID=entrezIDs)
write.table(out,file="id.txt",sep="\t",quote=F,row.names=F)
library("clusterProfiler")
library("org.Hs.eg.db")
library("enrichplot")
library("ggplot2")
pvalueFilter=0.05       
qvalueFilter=0.05        
rt=read.table("id.txt",sep="\t",header=T,check.names=F)      
rt=rt[is.na(rt[,"entrezID"])==F,]                           
gene=rt$entrezID
geneFC=2^rt$logFC
names(geneFC)=gene
colorSel="qvalue"
if(qvalueFilter>0.05){
  colorSel="pvalue"
}
kk=enrichGO(gene = gene,OrgDb = org.Hs.eg.db, pvalueCutoff =2, qvalueCutoff = 1, ont="all", readable =T)
GO=as.data.frame(kk)
GO=GO[(GO$pvalue<pvalueFilter & GO$qvalue<qvalueFilter),]
write.table(GO,file="GO.txt",sep="\t",quote=F,row.names = F)
GO<-read.table("GO.txt",sep="\t",header=T,check.names=F)
showNum=10
if(nrow(GO)<30){
  showNum=nrow(GO)
}
pdf(file="bubble.pdf",width = 9,height = 9)
bub=dotplot(kk,showCategory = showNum, orderBy = "GeneRatio",split="ONTOLOGY", color = colorSel) + facet_grid(ONTOLOGY~., scale='free')
print(bub)
dev.off()
pdf(file="circos.pdf",width = 15,height = 10)
cnet=cnetplot(kk, foldChange=geneFC, showCategory = 5, circular = TRUE, colorEdge = TRUE)
print(cnet)
dev.off()
library(sparcl)
library(Rtsne)
library(ggplot2)
inputFile="ssgseaOut-T-all.txt"     
data=read.table(inputFile, header=T, sep="\t", check.names=F, row.names=1)
hc=hclust(dist(t(data)))
k=2
y=cutree(hc, k)
median1=median(as.matrix(data[,y==1]))
median2=median(as.matrix(data[,y==2]))
if(median1>median2){
  y1=ifelse(y==1, "Immunity_H", "Immunity_L")
}else{
  y1=ifelse(y==1, "Immunity_L", "Immunity_H")
}
write.table(y1, file="cluster.txt", sep="\t", quote=F, col.names=F)
y2=ifelse(y1=="Immunity_H", "red", "blue")
pdf(file="hclust.pdf", width=30, height=15)
ColorDendrogram(hc, y=y2,
                labels=names(y), branchlength=0.3, 
                xlab=" ", sub=" ", main = " ")
dev.off()
tsneOut=Rtsne(t(data), dims=2, perplexity=10, verbose=F, max_iter=500,check_duplicates=F)
tsne=data.frame(tSNE1 = tsneOut$Y[,1], tSNE2 = tsneOut$Y[,2], Subtype=y1)	
#绘制tSNE图
pdf(file="tSNE.pdf", width=6.5, height=5)       
p=ggplot(data = tsne, aes(tSNE1, tSNE2)) + geom_point(aes(color = Subtype)) +
  scale_colour_manual(name="Subtype",  values =c("red", "blue"))+
  theme_bw()+
  theme(plot.margin=unit(rep(1.5,4),'lines'))+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
print(p)
dev.off()

library(ggplot2)          
inputFile="all-L.txt"      
outFile="vol-L.pdf"          
logFCfilter=1               
pValue=0.05              
rt=read.table(inputFile,sep="\t",header=T,check.names=F)
Significant=ifelse((rt$pValue<pValue & abs(rt$logFC)>logFCfilter), ifelse(rt$logFC>logFCfilter,"Up","Down"), "Not")
p = ggplot(rt, aes(logFC, -log10(fdr)))+
  geom_point(aes(col=Significant))+
  scale_color_manual(values=c("green", "black", "red"))+
  labs(title = " ")+
  theme(plot.title = element_text(size = 16, hjust = 0.5, face = "bold"))
p=p+theme_bw()
pdf(outFile,width=5.5,height=4.5)
print(p)
dev.off()