生信技能树——GEO芯片数据的合并

GSE83521和GSE89143数据合并

1.下载数据

rm(list = ls())
library(GEOquery)
library(stringr)
gse = "GSE83521"
eSet1 <- getGEO("GSE83521", 
                destdir = '.', 
                getGPL = F)
eSet2 <- getGEO("GSE89143", 
                destdir = '.', 
                getGPL = F)
#(1)提取表达矩阵exp
exp1 <- exprs(eSet1[[1]])
exp1[1:4,1:4]
exp2 <- exprs(eSet2[[1]])
exp2[1:4,1:4]
exp2 = log2(exp2+1)
table(rownames(exp1) %in% rownames(exp2))
length(intersect(rownames(exp1),rownames(exp2)))
exp1 <- exp1[intersect(rownames(exp1),rownames(exp2)),]
exp2 <- exp2[intersect(rownames(exp1),rownames(exp2)),]
boxplot(exp1)
boxplot(exp2)

#(2)提取临床信息
pd1 <- pData(eSet1[[1]])
pd2 <- pData(eSet2[[1]])
if(!identical(rownames(pd1),colnames(exp1))) exp1 = exp1[,match(rownames(pd1),colnames(exp1))]
if(!identical(rownames(pd2),colnames(exp2))) exp2 = exp2[,match(rownames(pd2),colnames(exp2))]

#(3)提取芯片平台编号
gpl <- eSet2[[1]]@annotation

#(4)合并表达矩阵
# exp2的第三个样本有些异常，可以去掉或者用normalizeBetweenArrays标准化，把它拉回正常水平。

exp2 = exp2[,-3]

exp = cbind(exp1,exp2)
boxplot(exp)
Group1 = ifelse(str_detect(pd1$title,"Tumour"),"Tumour","Normal")
Group2 = ifelse(str_detect(pd2$source_name_ch1,"Paracancerous"),"Normal","Tumour")[-3]

Group = c(Group1,Group2)
table(Group)
Group = factor(Group,levels = c("Normal","Tumour"))
save(gse,Group,exp,gpl,file = "")

两个数据集样本的情况

合并后的数据

2.针对不同数据集数据的差异，需要处理批次效应

2.1 使用limma包里的removeBatchEffect()函数

rm(list = ls())
load("")
#处理批次效应
library(limma)
#?removeBatchEffect()
batch <- c(rep("A",12),rep("B",5))
exp2 <- removeBatchEffect(exp, batch)
par(mfrow=c(1,2))  # 展示的图片为一行两列
boxplot((exp),main="Original")
boxplot((exp2),main="Batch corrected")

2.2 使用sva包中的combat() 函数

rm(list = ls())
load("")
#处理批次效应(combat)
library(sva)
#?ComBat

batch <- c(rep("A",12),rep("B",5))
mod = (~Group)
exp2 = ComBat(dat=exp, batch=batch, 
              mod=mod, =TRUE, ="A")
par(mfrow=c(1,2))
boxplot((exp),main="Original")
boxplot((exp2),main="Batch corrected")