Thanks a lot, James for your help.
That seems pretty straightforward.

That said, both ExpressionSets and MArrayLM objects (the output from
eBayes()) can be subset using the conventional square-bracket functions in
R. So for example, you could remove the first ten probesets from your fit2
object thusly:
fit2 <- fit2[-c(1:10),]
or you could create an indicator of TRUE/FALSE, based on some metric
ind <- fit2$p.value < 0.25
fit2 <- fit2[ind,]
The same thing can be done to the ExpressionSet object as well."

If I know the probe ID's for the probes I want to select or exclude from
the MArrayLM object (i.e. fit2) before producing the topTable() list,
can I also use probe ID's somehow to select or exclude from the MArryaLM
object?

Mark



On Wed, Nov 23, 2011 at 11:01 AM, James W. MacDonald
<jmacdon@med.umich.edu>wrote:

> Hi Mark,
>
>
> On 11/23/2011 1:00 PM, Mark Baumeister wrote:
>
>> Hi all,
>>
>> I am new to this list and have a question (below) related to -
>> selecting/filtering probesets from exprSet object prior to diff. exp.
>> anal.
>>
>> I'm also new to Bioconductor and am currently learning preprocessing of
>> microarray data (i.e. raw CEL files from the Affymetrix UG-133A array) and
>> then working
>> with the normlized exprSet object to detect differential gene expression
>> of
>> tumor
>> (ovarian) samples compared with normal samples.  I am currently working
>> with a set
>> of ~33 tumor samples and ~7 normal samples.
>>
>> Because my machine is 32 bit and cannot handle that much memmory
>> allocation,
>> for the preprocessing I am using a program called RMAExpress to produce
>> the
>> normalized exprSet object.  With the exprSet object (I am calling "eset")
>> I
>> am then using
>> Bioconductor for the differential gene expression analysis.
>>
>> To start I have been creating a desgin matrix (as below)
>> (which I name "design") for linear modeling steps I am using
>> that come with the limma package.
>>
>>  Normal Tumor
>> T1   0  1
>> T2   0  1
>> T3   0  1
>> T5   0  1
>> T7   0  1
>> N1  1  0
>> T8   0  1
>> T9   0  1
>> T10 0  1
>> T11 0  1
>> N2  1  0
>> T12 0  1
>> T13 0  1
>> T14 0  1
>> T15 0  1
>> N3  1  0
>>
>>
>>
>> and then I am using the following code to produce a linear model, a
>> contrast matrix,
>> and a list of differentially expressed genes.
>>
>>
>> fit<- lmFit(eset, design)
>> cont.matrix<- makeContrasts(NormalvsTumor=**Tumor-Normal, levels=design)
>> fit2<- contrasts.fit(fit, cont.matrix)
>> fit2<- eBayes(fit2)
>> topTable(fit2, number=100, adjust="BH") # use BH method
>>
>> My question is this,
>> Is there a way to select or exclude ceratin probesets that I want or don't
>> want to be included in the
>> linear model before I produce the list (topTable) of differentially
>> expressed genes?
>>
>
> There are ways to do this, but note that the eBayes() step above is
> estimating a prior for the probeset variance that uses all probesets on the
> array. If you selectively remove some probesets (say, all the low-variance
> probesets), you will be biasing the prior, which may have unintended
> effects.
>
> That said, both ExpressionSets and MArrayLM objects (the output from
> eBayes()) can be subset using the conventional square-bracket functions in
> R. So for example, you could remove the first ten probesets from your fit2
> object thusly:
>
> fit2 <- fit2[-c(1:10),]
>
> or you could create an indicator of TRUE/FALSE, based on some metric
>
> ind <- fit2$p.value < 0.25
>
> fit2 <- fit2[ind,]
>
> The same thing can be done to the ExpressionSet object as well.
>
> Best,
>
> Jim
>
>
>
>
>> I have looked at the genefilter function but have not found specific
>> examples of how to do what I want.
>>
>>
>> Thanks in advance,
>> -M
>>
>>
>>
>>
>>
> --
> James W. MacDonald, M.S.
> Biostatistician
> Douglas Lab
> University of Michigan
> Department of Human Genetics
> 5912 Buhl
> 1241 E. Catherine St.
> Ann Arbor MI 48109-5618
> 734-615-7826
>
> ************************************************************
> Electronic Mail is not secure, may not be read every day, and should not
> be used for urgent or sensitive issues
>



-- 
Mark Baumeister

http://sites.google.com/site/lfmmab/

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