Hi Tefina,

It sounds like you are in a very difficult situation (i.e. if the design is
such that batch separates condition exactly).  If the condition is nested
within batch or vice versa you may be able to minimize the effect, but
otherwise it may be a wash.  If you can address an effect with limma,
usually it will be possible to address it more effectively with ComBat, but
if you cannot separate batch from condition then even something like SVA
won't work.

I don't suppose you have any replicates that were run within each batch?
 It's a long shot, but if you did, you could use those to estimate the
batch effects.

You might consider something like fRMA (instead of say RMA) to leverage
other peoples' non-confounded designs.  That's the best I can think of;
hopefully one of the authors will happen upon your post and comment whether
this makes sense given your situation.  I would say something about
experimental design but obviously you are well aware of its importance
given your question.  Hopefully you can use fRMA or tech reps to salvage
it.

Best of luck,

--t


On Thu, Mar 8, 2012 at 2:56 AM, tefina <tefina.paloma@gmail.com> wrote:

> Dear List,
>
> I have microrarray data where condition is completely confounded with a
> time
> batch effect. When doing a PCA on the RMA normalized data, the first
> principal
> component separates clearly the two batches.
>
> What option do I have when I still want to compare different conditions
> across
> batches?
> As far as I understood I can't dissolve this batch effect neither with
> limma nor
> with e.g. ComBat.
> Shall I just go on with the limma analysis and keep in mind that some
> genes just
> might pop up due to batch effects?
>
> best,
> tefina
>
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-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

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