[BioC] Limma final gene expression report

[Ankit Pal]

Dear Mick,
Thanks a lot for the reply.
I am interested in the spots individually but for
further analysis of the spots I need a single
representative value for each gene.
I have looked up the manual, I did not find a way to
combine replicate spots into a single value.
Could you tell me what is the method or which section
of the manual is it present in.
t will be of great help to me.
Thank you,
-Ankit


--- "michael watson (IAH-C)"
<michael.watson at bbsrc.ac.uk> wrote:
> 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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> 
>