Hi,
  My preference is not to get into the discussion of expressed or
non-expressed as those terms relate to the mRNA, and all we see is whether
something stuck to a particular spot (which we think we know the sequence
of).  And also that is somewhat irrelevant to most questions (not all
though).  A somewhat simpler criteria is whether or not the spot/gene varies
across samples.  If it does not, then it does not have any information about
any phenotype and it is not clear that you would be interested in modeling
with it.  Certainly all non-expressed genes (modulo artifacts) will have
relatively constant expression.

   best wishes
     Robert


On Thu, Jan 15, 2009 at 2:49 AM, Daniel Brewer <daniel.brewer@icr.ac.uk>wrote:

> Thanks for the brilliant answer.  Very interesting stuff.  The only
> other question I would like to ask concerning this is when do you define
> a probe as non-expressed?  Is this done by observation of some kind of
> plot e.g. MA plot, a fixed percentage of probes or some absolute value
> known by experience.  For Affy arrays you can use the DaBG results but I
> am not sure what the correct approach would be with two colour microarrays.
>
> Many thanks
>
> Dan
>
> Gordon Smyth wrote:
> > Dear Dan,
> >
> > It's very common practice to keep all the probes for normalization, then
> > to filter control probes and consistently non-expressed probes before
> > differential expression analysis.  I recommend and do it this myself.
> > It's such common practice that it's surprising to see a paper on it at
> > this stage.
> >
> > It is in the spirit of normalization methods that all probes should be
> > retained for normalization, except in unusual cases in which some probes
> > are obviously poor quality for reasons other than expression level.
> >
> > At the differential expression step, probes can be usefully filtered out
> > if they are not of any potential interest.  This means control probes,
> > or probes which appear to be non-expressed across all conditions in the
> > experiment, i.e., on all arrays. I have frequently complained on this
> > mailing list about the practice of filtering individual low intensity
> > probes on individual arrays, which IMO is a very destructive practice.
> > If you filter a probe on the basis of expression, it must be filtered on
> > all arrays.
> >
> > Filtering non-expressed probes tends not be emphasised on this list
> > because users of this list are often sophisticated enough to use
> > variance stabilizing normalization methods such as rma, vsn, normexp or
> > vst.  This means that low-expression filtering is done more for
> > multiplicity issues than for variance stabilization, and therefore often
> > doesn't make a huge difference.  When using earlier normalization
> > methods such as MAS for Affy or local background correction for
> > two-color arrays, expression-filtering is absolutely essential, because
> > the normalized expression values are so unstable at low intensity levels.
> >
> > To James, it is not necessary to give retain all the probes on the array
> > for eBayes().  The only requirement is that eBayes() sees all the probes
> > which are under consideration for differential expression.  So filtering
> > out consistently non-expressed probes before linear modelling is
> > generally a good idea.  In fact, filtering often improves the eBayes()
> > assumptions. eBayes assumes that the residual variances are not
> > intensity-dependent. However very lowly expressed probes often follow a
> > mean-variance relationship which is somewhat different from the other
> > probes, even after variance stabilization, in which case filtering will
> > improve the constancy of variance assumption.  This tends not to be a
> > big issue with rma-Affy data, but it is an important issue with
> > vst-Illumina data for example.
> >
> > Best wishes
> > Gordon
>
> --
> **************************************************************
>
> Daniel Brewer, Ph.D.
>
> Institute of Cancer Research
> Molecular Carcinogenesis
> MUCRC
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>
> Tel: +44 (0) 20 8722 4109
>
> Email: daniel.brewer@icr.ac.uk
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-- 
Robert Gentleman, PhD
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M2-B876
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