MetaDE (version 1.0.5)

MetaDE.filter: A function to filter genes

Description

MetaDE.filter filters genes in the gene expression data sets.

Usage

MetaDE.filter(x, DelPerc)

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

DelPerc

a numeric vector of size 2, which specify the percentage of genes to be filtered in the two sequential steps of gene filtering. see "Details".

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.

Details

Two sequential steps of gene filtering were performed in MetaDE.filter. In the first step, we filtered out genes with very low gene expression that were identified with small average expression values across majority of studies. Specifically, mean intensities of each gene across all samples in each study were calculated and the corresponding ranks were obtained. The sum of such ranks across five studies of each gene was calculated and genes with the lowest alpha percent rank sum were considered un-expressed genes (i.e. small expression intensities) and were filtered out. Similarly, in the second step, we filtered out non-informative (small variation) genes by replacing mean intensity in the first step with standard deviation. Genes with the lowest beta percent rank sum of standard deviations were filtered out.

References

Xingbin Wang, Yan Lin, Chi Song, Etienne Sibille* and George C Tseng*. (2012) Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: with application to major depressive disorder. BMC Bioinformatics. tentatively accepted.

See Also

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

Examples

#================Example Test Filter.gene================================================#
label1<-rep(0:1,each=5)
label2<-rep(0:1,each=5)
exp1<-cbind(matrix(rnorm(5*200),200,5),matrix(rnorm(5*200,2),200,5))
exp2<-cbind(matrix(rnorm(5*300),300,5),matrix(rnorm(5*300,1.5),300,5))
rownames(exp1)<-paste("g1",1:200,sep="_")
rownames(exp2)<-paste("g2",1:300,sep="_")
symbol1<-sample(paste("symbol_",1:20,sep=""),200,replace=TRUE)
symbol2<-sample(paste("symbol_",1:20,sep=""),300,replace=TRUE)
study1<-cbind(c(NA,symbol1),rbind(label1,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<-MetaDE.Read(c("study1","study2"),via="txt",skip=c(2,1),log=FALSE)
mydata.matched<-MetaDE.match(mydata,"IQR")
mydata.Merged<-MetaDE.merge(mydata.matched)
mydata.filtered<-MetaDE.filter(mydata.Merged,DelPerc=c(0.1,0.2))
ind.res<-ind.analysis(mydata.filtered,ind.method=c("regt","regt"),tail="abs",nperm=10)
meta.res<-MetaDE.rawdata(mydata.filtered,ind.method=c("regt","regt"),meta.method="Fisher",ind.tail="abs",nperm=10,paired=rep(FALSE,2))