Sorry, I missed to reply to bioconductor ...

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.

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:

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  ??
2. geneIds = cluster of B    , universeGenes = ALL  but seems like I should
use universeGenes = A+B  ?? or only B?

Does it make sense to look at unexpressed genes ? seems like this might be
not very wise on the affy arrays.


Marco


On 1/11/07, marco zucchelli <marco.bioc@gmail.com> wrote:
>
> 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.
>
> 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:
>
> 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  ??
> 2. geneIds = cluster of B    , universeGenes = ALL  but seems like I
> should use universeGenes = A+B  ?? or only B?
>
> Does it make sense to look at unexpressed genes ? seems like this might be
> not very wise on the affy arrays.
>
>
> Marco
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> On 1/11/07, James W. MacDonald <jmacdon@med.umich.edu> wrote:
> >
> > marco zucchelli wrote:
> > > Hi,
> > >
> > >  I am using hyperGTest to test GO. Fot the universe of genes I use the
> >
> > > follwing:
> > >
> > > ENTREZ      <- as.list(hgu133plus2ENTREZID)
> > > univ        <- unlist(ENTREZ)
> > > univ        <- univ[!is.na(univ)]
> > >
> > > now in univ there are 47,430 genes but only 19,871 are unique, since
> > the
> > > same gene can be hybrydized several times on the same array.
> > >
> > >
> > >>length(univ)
> > >
> > > [1] 47430
> > >
> > >>length(unique(univ))
> > >
> > > [1] 19871
> > >
> > >
> > > Is it correct to have repetitions or should a list of unique genes be
> > used?
> > > i.e. should I use:
> > >
> > > universeGeneIds=univ   or
> > > universeGeneIds=unique(univ)
> >
> > You want the unique genes. In addition, if you have done any
> > pre-filtering of the data to remove e.g., those genes that don't change
> > expression, you want to remove those from your universe as well. The
> > universe should only consist of unique genes that could have been
> > selected by whatever statistical test you used. BTW, the geneIds should
> > also be unique.
> >
> > Seth has made some changes to the GOstats vignette that should make all
> > of this quite clear:
> >
> >
> > http://www.bioconductor.org/packages/2.0/bioc/vignettes/GOstats/inst/doc/GOstatsHyperG.pdf
> >
> > Best,
> >
> > Jim
> >
> >
> > >
> > > in the GOHyperGParams ??
> > >
> > > Regards
> > >
> > > Marco
> > >
> > >       [[alternative HTML version deleted]]
> > >
> > > _______________________________________________
> > > Bioconductor mailing list
> > > Bioconductor@stat.math.ethz.ch
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> > > Search the archives:
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> >
> >
> > --
> > 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
> >
> >
> > **********************************************************
> > Electronic Mail is not secure, may not be read every day, and should not
> > be used for urgent or sensitive issues.
> >
>
>

	[[alternative HTML version deleted]]

