Dear list, I have a question regarding to nonspecific filtering prior to limma analysis. I've searched on google, but no answer. I have a microarray experiment with three groups: group1, group2 and group3.According to limma vignette, my analysis would be like this: f<-factor(targets$Target, levels=c("group1","group2","group3") design<-model.matrix(~0+f) colnames(design)<-c("group1","group2","group3") fit<-lmFit(eset,design) contrast.matrix<-makeContrasts(group2-group1,group3-group2,group3-grou p1,levels=design) fit2<-contrasts.fit(fit,contrast.matrix) fit2<-eBayes(fit2) topTable(fit2,coef=1,adjust="BH") #group2-group1 topTable(fit2,coef=2,adjust="BH") #group3-group2 topTable(fit2,coef=3,adjust="BH") #group3-group1 My question is regarding to nonspecific filtering prior to this limma analysis with several groups. I would like to filter by removing the control genes, the duplicate probesets pointing to the same EntrezID and the genes with low variance using the IQR function. I know that using nsFilter from genefilter package, I can achieve this: eset<-nsFilter(eset, require.entrez=TRUE, remove.dupEntrez=TRUE, var.func=IQR,var.cutoff=varCutoff,filterByQuantile=TRUE, feature.exclude="^AFFX") My question is the following: is it correct to use nsFilter on ALL samples like lmFit or it must be used for each contrast of interest? I mean, using all samples, some interesting genes that are expressed only in one group would be removed. However, if nsFilter is used for each contrast separately, these genes won't be removed....In any case, the genes removed would be different. What is the procedure to use geneFilter when there are several groups? Thanks again, Javier

Hi Javier, Javier P?rez Florido wrote: > Dear list, > I have a question regarding to nonspecific filtering prior to limma > analysis. I've searched on google, but no answer. > > I have a microarray experiment with three groups: group1, group2 and > group3.According to limma vignette, my analysis would be like this: > > f<-factor(targets$Target, levels=c("group1","group2","group3") > design<-model.matrix(~0+f) > colnames(design)<-c("group1","group2","group3") > fit<-lmFit(eset,design) > contrast.matrix<-makeContrasts(group2-group1,group3-group2,group3-gr oup1,levels=design) > > fit2<-contrasts.fit(fit,contrast.matrix) > fit2<-eBayes(fit2) > topTable(fit2,coef=1,adjust="BH") #group2-group1 > topTable(fit2,coef=2,adjust="BH") #group3-group2 > topTable(fit2,coef=3,adjust="BH") #group3-group1 > > My question is regarding to nonspecific filtering prior to this limma > analysis with several groups. I would like to filter by removing the > control genes, the duplicate probesets pointing to the same EntrezID and > the genes with low variance using the IQR function. I know that using > nsFilter from genefilter package, I can achieve this: > > eset<-nsFilter(eset, require.entrez=TRUE, remove.dupEntrez=TRUE, > var.func=IQR,var.cutoff=varCutoff,filterByQuantile=TRUE, > feature.exclude="^AFFX") > > My question is the following: is it correct to use nsFilter on ALL > samples like lmFit or it must be used for each contrast of interest? I > mean, using all samples, some interesting genes that are expressed only > in one group would be removed. However, if nsFilter is used for each > contrast separately, these genes won't be removed....In any case, the > genes removed would be different. > What is the procedure to use geneFilter when there are several groups? I am not sure what you mean here. nsFilter does not take any account of the groups and so it would not give different results for different contrasts. And that is the point of the ns part of the name. Filtering is not based on phenotype. I would not apply it separately to different subgroups, but rather apply it once to the data set I intend to analyze. best wishes Robert > > Thanks again, > Javier > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >