[BioC] Limma final gene expression report

[Sean Davis]

One other question--are these replicate spots (i.e., the same DNA) or 
different oligos/clones for the same gene?

Sean

On May 10, 2005, at 5:59 AM, Ankit Pal wrote:

> 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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>>
>>
>>
>
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