Hi John,
 
I am also working with microarrays that have foreign/alien species probes on them, although in my case the platform is affymetrix, so single channel.  Assuming your foreign / negative control probes do not cross-hybridise with maize, they could be identified in two ways:
(a) as having lower signal generally than your maize probes (but remember a % of your maize probes may not be expressed in your samples or have low expression anyway) and
(b) as not being differentially expressed (again, a % of your maize probes will also not be differentially expressed).
 
What I've done is take both of these factors into account with two non-specific filters that also efficiently remove my negative/foreign species probes:
 
(1) Use kOverA in the genefilter package to filter out those genes with expression below a certain level (which can be determined with the package mixtools).
(2) Create a subset of your expression-set with probes that passed the "expression level" filter.
(3) Create a vector containing the row-wise variance for for each probe (first, do this separately for your maize probes and your foreign species probes, then compare the mean variance between foreign species (should be lower) and maize to get a minimum variance cut-off).
(4) Create a vector containing the row-wise variance for each probe (this time, all probes together) and use kOverA with your minimum variance cut-off to filter them.
(5) Create a subset of your first subset, this time using the probes that passed your second filter.  This subset should now contain only probes that are above background expression levels and likely (due to higher variance) to be differentially expressed.
(6) You can check to see if this subset still contains any of your foriegn species (in my case, after applying both filters I only had 2 foreign probes left out of 5,000 - and these were known to cross-hybridise with my study species).
 
All the above steps were easy for me to compute because I was using single channel data - so if you do have separate intensities for both your channels I would recommend starting with these and treating the dataset as if it were single channel.  Then for the limma part - if it is possible to recombine the single channel data into ratios, you could do this and proceed with your original loop design.  If not, have a look at what it says re single channel analysis of two colour data in the limma guide (p48), also handling direct designs (p31) and paired samples (p38) ... no idea how easy/difficult this would be but I guess some combination of those should give you the design you are looking for.
 
If you can make your expression set single channel, and would like to try the above, I can send you the code for each step - just let me know.  I found this useful, at least it gave me a topTable that I am satisfied with ;).
 
Just a word of warning though; if lots of your foreign species probes are cross-hybridising with your study species, the above filters would not necessarily remove them from your data (and in that case they would also not be true "negative controls" - although it is less likely that you would get lots of cross-hybs with the foriegn long oligos in the first place).
 
Also, if the proportion of DE genes in your study species is very low, it can be difficult to assign the variance cut-off (because variance in the foreign probes alone will not be that different from variance in the study species probes if most of the latter are not differentially expressed).      
 
Finally, either of the above filters could be used independantly, but I think the variance/DE expression filter is probably a more sensible way to identify probes for filtering out than low expression alone.  In my case, I am looking for biomarkers, so didn't want DE genes that were low expressed as they would be difficult to use later.  However, if you are studying a biological system, a low expressed gene that is none-the-less is DE, could be important and interesting and you would not want to loose these during filtering.
 
Hope this helps,
Amy
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Message: 19
Date: Tue, 13 Mar 2007 13:09:35 -0700
From: John Fowler <fowlerj@science.oregonstate.edu>
Subject: [BioC] Signal above negative controls in a two-color
        experiment -    limma?
To: bioconductor@stat.math.ethz.ch
Message-ID: <p06240806c21cb38845b5@[128.193.141.33]>
Content-Type: text/plain; charset="us-ascii" ; format="flowed"

Hello,

I have searched in the FAQ and mailing list, but cannot find an
answer to my question.  I apologize if this has been asked before.

I have a series of hybs using a long-oligo spotted array for maize,
and am using two-color (Cy3 & Cy5-labelled) targets.  I would like to
generate a list of probes that detect signal that is 'above
background' for each target - that would be, significantly greater
than signal detected by the negative control probes on the array
(Aliens and Human sequences).  These would hopefully identify a set
of genes that are expressed vs not-detectably-expressed in my tissues
of interest.

I have a loop design, and am using limma to identify differentially
expressed genes.  Is it possible to use limma to also do the analysis
I suggested above?  I assume that this would involve using the
separate channel functions of limma, but am unclear on how to
approach it.

Many thanks,
John

--
John Fowler                             Associate Professor
Botany and Plant Pathology (BPP) Dept.
2082 Cordley Hall                        Phone: (541) 737-5307
Oregon State University                  FAX: (541) 737-3573
Corvallis, OR  97331-2902  USA           Email: fowlerj@science.oregonstate.edu

 
********************************************************************************************************************************* 
Amy Mikhail
Research student
University of Aberdeen
Zoology Building
Tillydrone Avenue
Aberdeen
AB24 2TZ
Scotland
Email: a.mikhail@abdn.ac.uk
Phone: 00-44-1224-273256 (office)

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