[BioC] Another question about dye swap

[Giorgi, Elena]

Hi, 

Is it possible to include the dye effect for those genes for which it's
found to be significant and drop it for the others?

I guess I could separate the two groups of genes and define a different
design matrix for each group, but I'm wondering if there's also a way to
produce a "dynamic" kind of design matrix...

Thanks,
Elena


>>> "Gordon K Smyth" <smyth at wehi.EDU.AU> 03/01/06 11:46 PM >>>

> Date: Tue, 28 Feb 2006 15:02:32 +0200
> From: "Ron Ophir" <ron.ophir at weizmann.ac.il>
> Subject: [BioC] Another question about dye swap
> To: <bioconductor at stat.math.ethz.ch>
> Message-ID: <s404661c.050 at wisemail.weizmann.ac.il>
> Content-Type: text/plain; charset=US-ASCII
>
> Hi,
>>From limma user guide section 8.1.2 Simple comparisons -> Dye swaps
it
> is clear that for the following experimental design:
> FileName Cy3 Cy5
> File1        wt mu
> File2        mu wt
> File3        wt mu
> File4        mu wt
> *(four replicates of two groups (wt , mu) of which two replicates in
> each group is labeled by one color(red) the other two is labeled by
> another color (green))
> one can estimate the Dye effect by defining that effect as the
> intercept :
>  design <- cbind(DyeEffect=1,MUvsWT=c(1,-1,1,-1))
>  fit <- lmFit(MA, design)
>  fit <- eBayes(fit)
>
> Let's say that one finds that the Dye effect (for all genes) is
> statisticaly insignificant. Would it be correct than to force the
model
> to go through the intercept by
>
> design <- cbind(DyeEffect=-1,MUvsWT=c(1,-1,1,-1))
>
> Will I get a better estimation of the Mu to wt ratio?
>
> Thanks,
> Ron

No.  The code you give does not force the model through the intercept
and would give erroneous
estimates.  Simply omitting the DyeEffect column does what you want.

Gordon

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