library(Biobase)
library(GEOquery)
library(limma)
## load series and platform data from GEO ##不需改动
gset <- getGEO("GSE66660", GSEMatrix =TRUE, AnnotGPL=TRUE)
if (length(gset) > 1) idx <- grep("GPL11532", attr(gset, "names")) else idx <- 1
gset <- gset[[idx]]
## make proper column names to match toptable ##不需改动
fvarLabels(gset) <- make.names(fvarLabels(gset))
## group names for all samples ##不需改动
gsms <- "11001100"
sml <- c()
for (i in 1:nchar(gsms)) { sml[i] <- substr(gsms,i,i) }
## log2 transform（RMA方法） ##采用RMA方法则不需改动，可改为mas5等方法
ex <- exprs(gset)
qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
LogC <- (qx[5] > 100) ||
(qx[6]-qx[1] > 50 && qx[2] > 0) ||
(qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)
if (LogC) { ex[which(ex <= 0)] <- NaN
exprs(gset) <- log2(ex) }
## set up the data and proceed with analysis
sml <- paste("G", sml, sep="") ## set group names
fl <- as.factor(sml)
gset$description <- fl
design <- model.matrix(~ description + 0, gset)
colnames(design) <- levels(fl)
fit <- lmFit(gset, design)
cont.matrix <- makeContrasts(G1-G0, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2) ##原文为eBayes（fit2,0.01）
tT <- topTable(fit2, adjust="BH", sort.by="B", number=dim(gset)[1]) ##adjust.method是P.value成为adj.P.value所采用的方法，number是tT中包含的基因数目（可自行设置，我这个就是显示所有）
tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title","Gene.ID")) ##不要改动顺序
write.csv(tT, paste("all genes(T).xls"),quote=FALSE, sep="\t") ##原文为write.table(tT, file=stdout(), row.names=F, sep="\t")，两者均可，个人喜欢改动后的，简单易懂
#######一定要注意：上面保存的是全部基因！！下面我进行差异基因筛选：p<0.05,|logFC|>2。是原文里没有的！
degup<-tT[tT[,"adj.P.Val"]<0.05,]
degup<-degup[degup[,"logFC"]>2,] ##上调基因
write.csv(degup,paste("GSE66600DEGs(upregulate).xls"),quote=FALSE,sep="\t")
degdown<-tT[tT[,"adj.P.Val"]<0.05,]
degdown<-degdown[degdown[,"logFC"]<(-2),] ##下调基因
write.csv(degdown,paste("GSE66600DEGs(downregulate).xls"),quote=FALSE,sep="\t")
gene=as.character(tT[,1])
OrgDb=org.Hs.eg.db P.Value
require(DOSE)
require(clusterProfiler)
ekk <- enrichKEGG(gene=gene,organism="human",pvalueCutoff=0.01)
ego <- enrichGO(gene=gene,OrgDb="org.Hs.eg.db",ont="CC",pvalueCutoff=0.01,readable=TRUE)
write.csv(summary(ekk),"KEGG-enrich.csv",row.names =F)
write.csv(summary(ego),"GO-enrich.csv",row.names =F)
ego <- enrichGO(gene = gene,
universe = names(geneList),
OrgDb = org.Hs.eg.db,
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
ego2 <- enrichGO(gene = gene.df$ENSEMBL,
OrgDb = org.Hs.eg.db,
keytype = 'ENSEMBL',
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
ego3 <- enrichGO(gene = gene.df$SYMBOL,
OrgDb = “org.Hs.eg.db”,
keytype = 'SYMBOL',
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
ego <- enrichGO(gene = DEG$,
OrgDb = "org.Hs.eg.db",
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
可以把 CC BP MF 改成 ALL
ego <- enrichGO(gene=gene,OrgDb="org.Hs.eg.db",ont="ALL",pvalueCutoff=0.01,readable=TRUE)
ekk <- enrichKEGG(gene = eg$ENTREZID, organism ="human",keyType ="kegg", pAdjustMethod ="BH", pvalueCutoff =0.01, qvalueCutoff =0.05)
DO<-enrichDO(gene=DEG$Gene.ID, ont = "DO", pvalueCutoff = 0.01, pAdjustMethod = "BH",qvalueCutoff = 0.05)
eg=bitr(geneID = PP$geneID, "ENTREZID", "SYMBOL", "org.Hs.eg.db")
df.id<-bitr(df$SYMBOL, fromType ="SYMBOL", toType ="ENTREZID",OrgDb ="org.Hs.eg.db")
easy.df<-merge(df,df.id,by="SYMBOL",all=F)
sortdf<-easy.df[order(easy.df$foldChange, decreasing =T),]
gene.expr = sortdf$foldChange
names(gene.expr) <- sortdf$ENTREZID
edox <- setReadable(edo,'org.Hs.eg.db','ENTREZID')
forestplot(as.matrix(dat[,1:5]),mean = dat$V6,lower = dat$V7,upper = dat$V8,graph.pos= 5, graphwidth = unit(50,"mm"), is.summary = c(TRUE,rep(FALSE,6)),zero = 1,boxsize = 0.5,lineheight = unit(8,'mm'),colgap = unit(2,'mm'),lwd.zero = 3,lwd.ci =2,col=fpColors(box='#458B00',summary="#8B008B",lines = 'black',zero = '#7AC5CD'),xlab="The estimates",lwd.xaxis=3,lty.ci = "solid")
