0th

Percentile

##### Merge microarray data sets

Merge microarray data sets in possibly irregular order.

##### Usage
MetaDE.merge(x,MVperc=0)
##### Arguments
x
a list of studies. Each study is a list with components:
• x: the gene expression matrix.
• y: the outcome variable. For a binary outcome, 0 refers to "normal" and 1 to "diseased". For a multiple class outcome, the first leve
MVperc
a threshold to specificy which genes are remained for further analysis. The default is zero.
##### Details

The gene expression data sets may be in possibly irregular order with different numbers of genes. This function is used to extact the common genes across studies. The merged data sets have the same genes in the same order. When we combine a large of number of studies, the number of common genes may be very samll, so we allow to include somes gene appearing in most studies and missing in few studies. The default is zero which means that we only include genes appearing in all the studies.

##### Value

• a list of studies. Each study is a list with components:
• x: the gene expression matrix.
• y: the outcome.
• censoring.status: the censoring status. This only for survival data.

MetaDE.Read, MetaDE.filter,ind.analysis and MetaDE.rawdata

##### Examples
#================example test MetaDE.merge========================================================#
label1<-rep(0:1,each=5)
label2<-rep(0:1,each=5)
time1=c(4,3,1,1,2,2,3,10,5,4)
event1=c(1,1,1,0,1,1,0,0,0,1)
exp1<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5))
exp2<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,1.5),20,5))
rownames(exp1)<-paste("g1",1:20,sep="_")
rownames(exp2)<-paste("g2",1:20,sep="_")
symbol1<-sample(c("SST","VGF","CNP","LPA"),20,replace=TRUE)
symbol2<-sample(c("SST","VGF","CNP","APOE"),20,replace=TRUE)
study1<-cbind(c(NA,NA,symbol1),rbind(rbind(time1,event1),exp1))
study2<-cbind(c(NA,symbol2),rbind(label2,exp2))
setwd(tempdir())
write.table(study1,"study1.txt",sep="t")
write.table(study2,"study2.txt",sep="t")
mydata.merged<-MetaDE.merge(mydata.matched)