GEO差异分析

logFoldChange=1
adjustP=0.05

library(limma)
setwd("C:\\Users\\lexb4\\Desktop\\geoBatch\\07.diff")
rt=read.table("lncRNA.txt",sep="\t",header=T,check.names=F)
#重复基因取均值，去重复
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
rt=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
rt=avereps(rt)
rt=rt[rowMeans(rt)>0,]
#数据集矫正，标准化
rt=normalizeBetweenArrays(as.matrix(rt))
boxplot(rt)
#rt=log2(rt+1)
#差异分析
modType=c(rep("con",182),rep("treat",32))
design <- model.matrix(~0+factor(modType))
colnames(design) <- c("con","treat")
exprSet=rt #exprSet 为表达矩阵
rownames(design)=colnames(exprSet) #更改分组信息design行名为分组名，也就是样本名字
#构造了design矩阵，得出每一个样本属于哪一个组，属于为1，不属于为0
fit <- lmFit(rt,design)
#自定义比较元素
contrast.matrix<-makeContrasts(treat-con,levels=design)
#自定义函数
deg = function(exprSet,design,contrast.matrix){#定义一个叫deg的函数
  ##step1#logFC后是前的多少倍,进行差异化比较
  fit <- lmFit(exprSet,design)
  ##step2
  fit2 <- contrasts.fit(fit, contrast.matrix) 
  ##这一步很重要，大家可以自行看看效果
  
  fit2 <- eBayes(fit2)  ## default no trend !!!
  ##eBayes() with trend=TRUE
  ##step3
  tempOutput = topTable(fit2, coef=1, n=Inf)
  nrDEG = na.omit(tempOutput) 
  #write.csv(nrDEG2,"limma_notrend.results.csv",quote = F)
  head(nrDEG)
  return(nrDEG)
}
deg = deg(exprSet,design,contrast.matrix)
head(deg)#查看前几行文件
save(deg,file = 'deg.Rdata')#存储文件
write.table(deg,file="limmaTab.xls",sep="\t",quote=F)

#write table
diffSig <- deg[with(deg, (abs(logFC)>logFoldChange & adj.P.Val < adjustP )), ]
write.table(diffSig,file="diff.xls",sep="\t",quote=F,row.names=T)
diffUp <- deg[with(deg, (logFC>logFoldChange & adj.P.Val < adjustP )), ]
write.table(diffUp,file="up.xls",sep="\t",quote=F,row.names=T)
diffDown <- deg[with(deg, (logFC<(-logFoldChange) & adj.P.Val < adjustP )), ]
write.table(diffDown,file="down.xls",sep="\t",quote=F,row.names=T)

#write expression level of diff gene
hmExp=rt[as.vector(diffSig[,1]),]
diffExp=rbind(id=colnames(hmExp),hmExp)
write.table(diffExp,file="diffExp.txt",sep="\t",quote=F,col.names=F)

#差异热图
if(T){ 
  # 每次都要检测数据,rt为表达数据，取过log的
  rt[1:4,1:4]
  #分组
  group_list<-c(rep("noTcells",182),rep("Tcells",32))
  #查看分组
  table(group_list)
  x=deg$logFC #deg取logFC这列并将其重新赋值给x
  names(x)=rownames(deg) #deg取probe_id这列，并将其作为名字给x
  cg=c(names(head(sort(x),100)),#对x进行从小到大排列，取前100及后100，并取其对应的探针名，作为向量赋值给cg
       names(tail(sort(x),100)))
  library(pheatmap)
  pheatmap(rt[cg,],show_colnames =F,show_rownames = F) #对dat按照cg取行，所得到的矩阵来画热图
  n=t(scale(t(rt[cg,])))#通过“scale”对log-ratio数值进行归一化，现在的dat是行名为探针，列名为样本名，由于scale这个函数应用在不同组数据间存在差异时，需要行名为样本，因此需要用t(dat[cg,])来转换，最后再转换回来
  
  n[n>2]=2
  n[n< -2]= -2
  n[1:4,1:4]
  pheatmap(n,show_colnames =F,show_rownames = F)
  ac=data.frame(g=group_list)
  rownames(ac)=colnames(n) #将ac的行名也就分组信息（是‘no TNBC’还是‘TNBC’）给到n的列名，即热图中位于上方的分组信息
  pheatmap(n,show_colnames =F,
           show_rownames = F,
           cluster_cols = F, 
           annotation_col=ac,filename = 'heatmap_top.pdf') #列名注释信息为ac即分组信息
  
  
}
#deg.csv是所有基因的差异表达情况
write.csv(deg,file = 'deg.csv')
dev.new()

#火山图
if(T){
  nrDEG=deg
  head(nrDEG)
  attach(nrDEG)
  plot(logFC,-log10(P.Value))
  library(ggpubr)
  df=nrDEG
  df$v= -log10(P.Value) #df新增加一列'v',值为-log10(P.Value)
  ggscatter(df, x = "logFC", y = "v",size=0.5)
  
  df$g=ifelse(df$P.Value>0.05,'stable
              ', #if 判断：如果这一基因的P.Value>0.05，则为stable基因
              ifelse( df$logFC >1,'up', #接上句else 否则：接下来开始判断那些P.Value<0.01的基因，再if 判断：如果logFC >1,则为up（上调）基因
                      ifelse( df$logFC < -1,'down','stable') )#接上句else 否则：接下来开始判断那些logFC <1 的基因，再if 判断：如果logFC <1，则为down（下调）基因，否则为stable基因
  )
  table(df$g)
  df$name=rownames(df)
  head(df)
  ggscatter(df, x = "logFC", y = "v",size=0.5,color = 'g')
  ggscatter(df, x = "logFC", y = "v", color = "g",size = 0.5,
            label = "name", repel = T,
            #label.select = rownames(df)[df$g != 'stable'] ,
            #label.select = c('TTC9', 'AQP3', 'CXCL11','PTGS2'), #挑选一些基因在图中显示出来
            palette = c("#00AFBB", "#E7B800", "#FC4E07") )
  ggsave('volcano.png')
  
  ggscatter(df, x = "AveExpr", y = "logFC",size = 0.2)
  df$p_c = ifelse(df$P.Value<0.001,'p<0.001',
                  ifelse(df$P.Value<0.01,'0.001<p<0.01','p>0.01'))
  table(df$p_c )
  ggscatter(df,x = "AveExpr", y = "logFC", color = "p_c",size=0.2, 
            palette = c("green", "red", "black") )
  ggsave('MA.pdf')
}