[BioC] question_gene list from venn diagram limma function for two color array data

[Francois Pepin]

Hi Jim,

you are correct, my code only looks at one contrast at a time, the same
way that topTable does it. Your modification is needed to look at the
intersection.

Francois

On Wed, 2006-11-01 at 14:36 -0500, James W. MacDonald wrote:
> Hi Francois,
> 
> Francois Pepin wrote:
> > Hi Seung-Min,
> > 
> > The differences come from the classifyTestsF. It classifies the genes
> > based on the F statistics, rather than the B-values which topTable uses.
> > 
> > Another way to get the information about the up genes both contrast
> > (Jim's way should work too):
> > 
> > fit$genes[which(results[,1]>0,] ##set to <0 for down genes
> 
> I don't think that will work. The first column of the results matrix 
> gives information about the genes in the first contrast only. To get 
> both contrasts, you need results[,1] > 0 & results[,2] > 0. In any case, 
> you have to look at both contrasts to find the genes for any given cell 
> of the Venn diagram.
> 
> Best,
> 
> Jim
> 
> 
> > 
> > Please note that, unlike topTable, the list is ordered by position on
> > the chip, not by how significant the differences are.
> > 
> > Francois
> > 
> > On Tue, 2006-10-31 at 16:25 -0800, lee wrote:
> > 
> >>Hello,
> >>  I am using my two color array data. I want to know the genes that
> >>are significantly Up or Down in both "HFEvsWT" and "SlavsWT" groups
> >>from Venn diagram results. I also want to know the genes that are only
> >>significantly Up or Down in one group. When I tried using the gene
> >>list from topTable function, I got different number of genes compared
> >>to Venn Diagram results. Thus, I want to know what are the genes after
> >>Venndiagram analysis.
> >>  Could you help me?
> >>  Thank you so much.classifyTestsF
> >>   
> >>  Sincerely, Seungmin Lee
> >>   
> >>   
> >>   
> >>   
> >>  library(limma)
> >>targets<-readTargets("Target4wkEnteroHFESla.txt")
> >>f<-function(x) as.numeric(x$Flags>-99)
> >>files<-targets[,c("FileName")]
> >>RG<-read.maimages(files,columns=list(R="F635 Mean",G="F532 Mean",Rb="B635 Median",Gb="F532 Median"),annotation=c("Block","Row","Column","ID","Accession","Symbol"))
> >>  plotMA(RG)
> >>RG$genes<-readGAL("meebo.gal")
> >>RG$printer<-getLayout(RG$genes)
> >>  MA.p<-normalizeWithinArrays(RG,method="loess")
> >>MA.pAq<-normalizeBetweenArrays(MA.p,method="Aquantile")
> >>design<-modelMatrix(targets,ref="WT.C57.chow")
> >>design
> >>contrast.matrix<-cbind("HFEvsWT"=c(1,0),"SlavsWT"=c(0,1))
> >>rownames(contrast.matrix)<-colnames(design)
> >>contrast.matrix
> >>fit<-lmFit(MA.pAq,design)
> >>fit2<-contrasts.fit(fit,contrast.matrix)
> >>fit2<-eBayes(fit2)
> >>  topTable(fit2,coef="HFEvsWT",adjust="BH")
> >>plotMA(fit2,array=1)
> >>  topTable(fit2,coef="SlavsWT",adjust="BH")
> >>plotMA(fit2,array=2)
> >>  results<-classifyTestsF(fit2, p.value=0.01)
> >>summary(results)
> >>table("HFEvsWT"=results[,1],"SlavsWT"=results[,2])
> >>vennDiagram(results,include="up")
> >>vennDiagram(results,include="down")
> >>
> >>
> >>-----------------------------------------------------------------------
> >>Seung-Min Lee
> >>
> >>graduate student
> >>244 Morgan Hall
> >>Molecular&Biochemical Nutrition
> >>University of California at Berkeley
> >>94720-3104
> >>lab phone (510)643-2351
> >>lab fax(510)642-0535
> >>
> >> 
> >>---------------------------------
> >>
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> >>
> > 
> > 
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>