James,

 thanks for your answer. It is very good to hear the ideas outside the own
lab, were people tend to follow a common way of thinking.
I am not very enthusiastic abuot MM probes, but in my own experiment I found
the following:

1/3 od the probes are always absent (12 arrays, 6 tissues, 2 replicates for
tissue), so feels like the calls are not random.

When I cross always absent probes (group A) with the ontologies, I find
10-15 ontologies that are heavily overrepresented (p-values 10E-30-10E-15).
Would this be an artifact, the probes with the same particular annotation
would generate a lot of absent calls, which seems a little bit unlikely?

Actually the ontologies overepresented have a also biological meaning (i.e.
something we would have expected).

When I look at group B, the largest cluster is (20,000 probes) is made up of
probes that are never differentially expressed (their expression is constant
over the arrays). When I tested for underepresentation I found that
basically  the same ontologies overepresented in A are underepresented in
group B, cluster:never differentially expressed.

At this point I made another experiment and divided all the probes on the
chips by the criterion always Absent (this is still group A) and always
present (on all the chips).
Then, as before I run limma on the always present probes and picked the
cluster of probes that do not show any differential expression (this time
about 10,000)
I tested those for underrepresentation and I found basically the same
ontologies as before.

So the conclusion was  that some GO are overepresented in the always absent
probes and undrepresented in the always present (with constant expression)

So it seems like the absent calls are not that bad ?


Marco


















On 1/11/07, James W. MacDonald <jmacdon@med.umich.edu> wrote:
>
> marco zucchelli wrote:
> > Sorry, I missed to reply to bioconductor ...
>
> Thanks for re-sending to the list.
>
> >
> > Dear James,
> >
> > actually I tried both the options and got the same result, which
> probably
> > menas that the hyperGTest is taking care of the repeats.
>
> True. It does that now. In addition, in the new version it does some
> validity checking to ensure that e.g., the geneId is a subset of the
> universeGeneIds.
>
> >
> > I use hgu133plus2.
> >
> > The pre-filtering is pretty interesting argument. In my case I have 2
> > biological replicates for each tissue and I split the probes based on
> the
> > following rule:
>
> Here I am assuming that you mean probesets when you say probes.
>
> >
> > 1.probes that are called absent on all the arrays (group A)
> > 2.probes that have consistent calls on the biological replicates (i.e.
> they
> > are either present or moderate or absent on both the replicates) but at
> > least once they are present on a pair of replicates (group B)
> > 3. others. these are probes that may be absent on one replicate and
> present
> > on the other etc. (group C)
> >
> > now I find differentially expressed gened with limma in group B and I
> > cluster them according to the pattern of up-down regulation they have on
>
> > the
> > different tissues.
> >
> > I am intrested also in genes that are always absent (group A) since this
> > may
> > be biologically of interest in my experiment.
> >
> > What is now a good universe to use?
> >
> > I filtered out probes that have no entrezID both in my gene lists and
> the
> > hgu133plus2 array, I got the entrezId's and eliminated the doubles by
> using
> > unique().
> >
> > Then I used:
> >
> > 1. geneIds = A                  , universeGenes = ALL  but seems like I
> > should use universeGenes = A+B  ??
>
> For this group, the statistic you used to select 'significant' probesets
> was the P/A calls, and you selected from all the genes on the chip, so I
> think your universe is correct.
>
> > 2. geneIds = cluster of B    , universeGenes = ALL  but seems like I
> should
> > use universeGenes = A+B  ?? or only B?
>
> Here you pre-filtered the probesets and selected only those that
> fulfilled your 'B' criterion. You then used a linear model to select
> differentially expressed genes from this subset of the chip, so IMO,
> your universe is the 'B' genes.
>
>
> >
> > Does it make sense to look at unexpressed genes ? seems like this might
> be
> > not very wise on the affy arrays.
>
> I think you might get ten different answers from ten different
> statisticians if you asked them about what it means when a probeset has
> an 'absent' call.
>
> I am personally not convinced that the P/M/A calls mean much, mainly
> because a large percentage (30 - 40%) of the MM probes are brighter than
> their matching PM probe. It seems to me that the MM probes capture an
> unknown mixture of noise, transcript abundance, background binding, and
> binding of unexpected transcript (e.g., an unrelated transcript for
> which the MM probe is a PM that Affy didn't know about).
>
> The assumption for the P/M/A calls is that the MM probe only captures
> noise and background binding, so if the PM probes are significantly
> brighter, then the transcript is expressed. In many cases this may well
> be true. However, I think there are likely enough cases where this isn't
> true that I am not comfortable assuming that an absent call means the
> gene really isn't being expressed.
>
> That said, by looking at the 'unexpressed' genes are you really trying
> to figure out which pathways (for lack of a better term) are being shut
> down, or not used? If that is the case, then maybe you could use the B
> genes and look for _underrepresented_ GO terms as well. This might give
> you what you want without having to make any assumptions about which
> genes are not being expressed.
>
> You can test for under-represented GO terms by setting the testDirection
> argument of your GOHyperGParams object to "under". So if your
> GOHyperGParams object were set up like this:
>
> params <- new("GOHyperGParams", geneIds = A, geneUniverseIds = ALL)
>
> then you would test for over representation as normal:
>
> hyperGTest(p)
>
> and under-representation like this:
>
> testDirection(params) <- "under"
> hyperGTest(p)
>
> HTH,
>
> Jim
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> Affymetrix and cDNA Microarray Core
> University of Michigan Cancer Center
> 1500 E. Medical Center Drive
> 7410 CCGC
> Ann Arbor MI 48109
> 734-647-5623
>
>
> **********************************************************
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>

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