[BioC] Limma final gene expression report

[michael watson (IAH-C)]

There are ways of combining replicate spots in limma, and it is all in the user guide :-)

However, many people, myself included, prefer things reported on a spot-by-spot basis.  If all replicate spots for a particular gene are reported as significant, I take that as further proof that i) the gene is differentially expressed, ii) my arrays are of good quality, iii) my experimental procedure was of good quality.  Think about the case where only one out of two spots is reported - is that because one of the spots was of poor quality?  Or because the values for each spot differ by a lot?  You would lose this valuable information if you just took the average between replicates.

If you *really* want an average value for each spot, simply take the average M value from the output of toTapble.

Mick


-----Original Message-----
From:	bioconductor-bounces at stat.math.ethz.ch on behalf of Ankit Pal
Sent:	Tue 10/05/2005 6:15 AM
To:	bioconductor at stat.math.ethz.ch
Cc:	
Subject:	[BioC] Limma final gene expression report

Dear All,
While looking at the Limma user guide, I came across
the following example

> targets <- readTargets("SwirlSample.txt")
> RG <- read.maimages(targets$FileName, source="spot")

> RG$genes <- readGAL()                     
> RG$printer <- getLayout(RG$genes)         
> MA <- normalizeWithinArrays(RG)           
> MA <- normalizeBetweenArrays(MA)          
> fit <- lmFit(MA, design=c(-1,1,-1,1))     
> fit <- eBayes(fit)                        
> options(digits=3)
> topTable(fit, n=30, adjust="fdr")         
ID        Name       M    A     t  P.Value    B
control   BMP2      -2.21 12.1 -21.1 0.000357 7.96
control   BMP2      -2.30 13.1 -20.3 0.000357 7.78
control   Dlx3      -2.18 13.3 -20.0 0.000357 7.71
control   Dlx3      -2.18 13.5 -19.6 0.000357 7.62
fb94h06 20-L12       1.27 12.0  14.1 0.002067 5.78
fb40h07  7-D14       1.35 13.8  13.5 0.002067 5.54

I have omitted a few rows and columns.
Here we see that after all the data transformations,
we get an output where the ranking for the probes in
an array is  done on the basis of the B value.
Notice that there are reapeating names for genes,
therefore for a set of replicates, within and across
arrays, each spot is reported separately as an
individual entity.
In the case of BMP2 from the above example, which
result do I consider?
Is there a way in which I can get a single result for
a set of replicates.
I am new to this package, so please do let me know if
there is a problem in my understanding the concept.
Thank you,
-Ankit

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