Claus,

Thank you for your response. However, at some points, I don't agree with
you.
The differentially expressed genes for just one group, I mean the genes
whose average expression levels across the biological replicates (here  4
replicates) are over/under the grand mean expression value. I think it's
similar as the analysis of identifying the differently expressed genes in
experiment of 4 replicates with two color arrays (Cy5 Mut, Cy3 Ref), which
you got single log ratios for each gene across 4 biological replicates. For
my design, I just measure the absolute expression value(single channel
intensity).
Therefore, when I fit the limma model, it actully evaluate the average
expression level(intensity) for each gene across the replicates.
Of course I may just rank the average intensities from high to low and
compare them with mean to get the idea of differently expressed genes. But I
believe limma can do better job, since I want not only ranking but also
significant level. If the variability is hight among the replicates, the
expression level for this gene maybe not reliable even the average is high
for this gene. I just try to figure out a way to separate the over/under
expressed values.

If I was wrong, please let me know. Thank you.



On Tue, Sep 15, 2009 at 12:55 PM, Mayer, Claus-Dieter <c.mayer@abdn.ac.uk>wrote:

> Hi,
>
> With only one group you can not speak of "differentially expressed" and
> testing, as that assumes that you have at least two different groups or
> conditions. The test that you have performed probably just compares gene
> expression to zero (a moderated one-sample t-test) and for that you would
> expect all genes to be significant.
>
> What you (I am guessing) probably mean by "differentially expressed" is
> that you are interested to find genes that vary highly between your 4
> replicates. To find those the best you can do is to rank the genes with
> respect to their variances/standard deviations. But you can't get a p-value
> for this, because (unless all values are identical) any gene will have a
> variance that is significantly higher than 0.
>
> Best Wishes
>
> Claus
>
> > -----Original Message-----
> > From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-
> > bounces@stat.math.ethz.ch] On Behalf Of zrl
> > Sent: 15 September 2009 17:18
> > To: Heidi Dvinge
> > Cc: bioconductor
> > Subject: Re: [BioC] question about topTable ranking of limma
> >
> > Sorry for the incomplete message, click the send accidentally.
> >
> > This analysis is for only one group of 4 biological replicates such as:
> >                        group
> > array1              a
> > array2              a
> > array3              a
> > array4              a
> >
> > I tried to identify the genes which are differently expressed in group a,
> > but no other reference groups for comparison. Therefore, even all the t
> > statistics are positive.
> >
> > Any thoughts? Thanks.
> >
> >
> >
> > On Tue, Sep 15, 2009 at 11:13 AM, zrl <zrl1974@gmail.com> wrote:
> >
> > > Hi Heidi,
> > >
> > > Thank you for your response. Maybe I didn't make my question very
> clear.
> > > This analysis is for only one group of 4 biological replicates such as:
> > >                        group
> > > array1
> > >
> > >
> > >
> > >
> > > On Tue, Sep 15, 2009 at 4:20 AM, Heidi Dvinge <heidi@ebi.ac.uk> wrote:
> > >
> > >>  Hello,
> > >> you can just sort the topTable result by the t-statistics since these
> > will
> > >> be either positive or negative, or call it directly with sort.by="t"
> > and
> > >> then filter for significant p-values.
> > >>
> > >> HTH
> > >> \Heidi
> > >>
> > >> On 15 Sep 2009, at 10:05, zrl wrote:
> > >>
> > >> Dear List,
> > >>
> > >> I have several biological replicates affy arrayes (a simple one group
> 4
> > >> arrayes), and tried to use eBayes to get the differentially expressed
> > >> genes.
> > >> The topTable ranked the genes by B statistics, which mixed over-
> > expressed
> > >> genes and under-expressed genes. My question is how I should separate
> > the
> > >> over and under expressed genes from topTable results. My idea is to
> > >> calculate the mean average expressed value/intensities (extracted from
> > >> topTable results with using the number of all the genes) and compare
> > >> ranked
> > >> genes with the mean value, if the expressed value is greater than the
> > >> mean,
> > >> I take this gene as over-expressed, otherwise, it's under-expressed.
> > >> Since I don't know the underlying implement of topTable or eBayes, I
> > want
> > >> to
> > >> make sure if my method is right. Or you have some better ideas.
> Thanks.
> > >>
> > >> [[alternative HTML version deleted]]
> > >>
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> > >>
> > >>
> > >>
> > >
> >
> >         [[alternative HTML version deleted]]
> >
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