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

[James W. MacDonald]

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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>>
> 
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623


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