kuangqin wrote:
>
>
> Dear Helper
>
>
> For the Estrogen data given in limma usersguide, we can estimate
estrogen effect and
> the time-estrogen interaction as follows
>
>> design=matrix(c(1,1,1,1,1,1,1,1,
> + 0,0,0,0,1,1,1,1,
> + 0,0,1,1,0,0,0,0,
> + 0,0,0,0,0,0,1,1),nrow=8,ncol=4, byrow=F)
>> rownames(design)= treatments
>> colnames(design)= c("Intercept","Time","E10","E48")
>>
>> design
> Intercept Time E10 E48
> e10 1 0 0 0
> e10 1 0 0 0
> E10 1 0 1 0
> E10 1 0 1 0
> e48 1 1 0 0
> e48 1 1 0 0
> E48 1 1 0 1
> E48 1 1 0 1
>> cont.matrix <-
cbind(E10=c(0,0,1,0),E48=c(0,0,0,1),"E48-E10"=c(0,0,1,-1) )
>> cont.matrix
> E10 E48 E48-E10
> [1,] 0 0 0
> [2,] 0 0 0
> [3,] 1 0 1
> [4,] 0 1 -1
>
> The first and second coefficients (in cont.matrix) estimate the
estrogen effect
> at 10 hours and 48 hours ,respectively. The third coefficient (in
cont.matrix)
> estimates the time-estrogen interaction.
>
> Here I want to ask another question:
>
> Which genes have estrogen effect (up/down between absent and
present)
> CONSISTENTLY at both 10 hours and 48 hours?
>
> I am not sure how to add one item in either design or contrast
matrix to do
> this job ???
>
> Is it an efficient way to just simply look the intersection of genes
selected in
> first and second coefficients (in cont.matrix)?
I think you want ?decideTests and ?vennDiagram. The decideTests()
function will output a matrix that you can use to identify the genes
that are up or down regulated in the intersection of the two
comparisons.
Best,
Jim
>
> Thank you very much for your help in advance !!!
>
> Qin Kuang
>
>
>
>
>
>
>
>
>
>
>
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--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
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7410 CCGC
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