I fixed the problem with eBayes....I used ebayes() and not eBayes()
reported in LIMMA help, "ebayes is the earlier and leaner function.
is intended to have a more object-orientated flavor as it produces
containing all the necessary components for downstream analysis. "
Then, I fixed the problem by using eBayes()
However, I did not manage to fix the problem of duplicateCorrelation()
When I define the design including the dye effect and I use this
duplicateCorrelation(), the calculated consensus correlation is NaN,
without including the dye effect is
-0.55 (I have a dye swap design).
I have tryed to "unswap" the design also (as you suggested in a
message), but I have obtained the same result, that is NaN.
I have used a different dataset with the same experimental design also
the result was the same: NaN
I have tryed to use the simple dye swap design for
then I have defined a new design
and I have used this together with the correlation previously
duplicateCorrelation (-0.55) for model computing
Is this a right way of mouving aroud the problem?
Any suggestion will be appreciated.
----- Original Message -----
From: "Gordon K Smyth" <email@example.com>
To: "Erika Melissari" <erika.melissari at="" bioclinica.unipi.it="">
Cc: "Bioconductor mailing list" <bioconductor at="" stat.math.ethz.ch="">
Sent: Saturday, January 24, 2009 03:40 AM
Subject: [BioC] Use of duplicateCorrelation
> Dear Erika,
> The output from fit() and eBayes() cannot be as you've given below,
> because eBayes() does not remove the $coefficient component of the
> model object. Can you please rerun your code from scratch in a new
> session, re-reading the data and so on.
> Best wishes
>> Date: Thu, 22 Jan 2009 15:51:43 +0100
>> From: "Erika Melissari" <erika.melissari at="" bioclinica.unipi.it="">
>> Subject: [BioC] Use of duplicateCorrelation
>> To: <bioconductor at="" stat.math.ethz.ch="">
>> Message-ID: <00c001c97ca0$f1a27fe0$ba517283 at maanalysis>
>> Content-Type: text/plain
>> Hello all,
>> I'm studing how to use duplicateCorrelation() of LIMMA package in
>> to evaluate the between-arrays correlation.
>> I have a simple experiment of direct comparison with dye-swap as
>> SlideNumber FileName Cy3 Cy5 Date
>> 1 Ag_1.gpr wt1 1RP
>> 2 Ag_2.gpr 1RP wt1
>> 3 Ag_3.gpr wt2 2RP
>> 4 Ag_4.gpr 2RP wt2
>> I use duplicateCorrelation() as follows:
>> design <- c(1,-1,1,-1)
>>  -0.5543286
>> The correlation is negative because of dye-swap.
>> Then, I evaluate linear model as explained in limma userguide:
>> Length Class Mode
>> coefficients 10807 -none- numeric
>> stdev.unscaled 10807 -none- numeric
>> sigma 10807 -none- numeric
>> df.residual 10807 -none- numeric
>> ndups 1 -none- numeric
>> spacing 1 -none- numeric
>> block 4 -none- numeric
>> correlation 1 -none- numeric
>> cov.coefficients 1 -none- numeric
>> pivot 1 -none- numeric
>> genes 5 data.frame list
>> Amean 10807 -none- numeric
>> method 1 -none- character
>> design 4 -none- numeric
>> Length Class Mode
>> df.prior 1 -none- numeric
>> s2.prior 1 -none- numeric
>> s2.post 10807 -none- numeric
>> t 10807 -none- numeric
>> p.value 10807 -none- numeric
>> var.prior 1 -none- numeric
>> lods 10807 -none- numeric
>> Error in dim(data) <- dim : attempt to set an attribute on NULL
>> What does it means this error message and, above all, where is the
>> mistake in my analysis procedure?
>> I do not understand why in fit2 there are not any coefficients!
>> I would like to evaluate the dye effect also. How can I do this?
>> I tryed the inclusion of a dye effect coefficient in the design,
>> but when I calculate duplicateCorrelation corfit$consensus is NaN.
>> Thanks very much for any kind of help in advance!
>> Best regards
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