Hi,

Will working at probe-level instead of probesets be of any help in this
situtaion (with two samples)?

Nicolas, may be you can try this: HTself: self-self based statistical test
for low-replication microarray studies* @
http://blasto.iq.usp.br/~rvencio/HTself/index.html
<http://http://blasto.iq.usp.br/%7Ervencio/HTself/index.html>*
Hope, this helps.
Regards,
Sharon


On 1/25/06, James W. MacDonald <jmacdon@med.umich.edu> wrote:
>
> Nicolas Servant wrote:
> > Thanks for your answer,
> > But in this case, i have to choose a fold change threshold ! And it is
> > supported that the FC tends to be greater at low expression levels.
> > For instance a FC greater than 2 for expression values near 50 is
> > readily seen, but it is low probability to observe FC greater than 2 for
> > expression values near 1000
> > So i would like to use a more robust approach.
>
> With only two samples, you are stuck with fold changes. However, you
> might be able to make your results more robust by filtering out those
> genes that you think are too small. I often use kOverA() in the
> genefilter package to do this.
>
> Best,
>
> Jim
>
>
> >
> > Regards,
> > Nicolas S.
> >
> > Sean Davis wrote:
> >
> >
> >>On 1/25/06 8:34 AM, "Nicolas Servant" <Nicolas.Servant@curie.fr> wrote:
> >>
> >>
> >>
> >>
> >>>Hello,
> >>>
> >>>Does anybody know a R package or function to compare expression level
> >>>(affy data) of two groups with no replicates in each group ? In fact,
> >>>just compare one array to an other.
> >>>The purpose is to find differentially expressed genes.
> >>>We cannot used statistical test (not enougth replicates), but we can
> >>>used graphical approach based on scatter plot, and outliers detection
> >>>approach.
> >>>
> >>>
> >>
> >>Simply take array A and divide it by array B.  Then rank the genes by
> those
> >>ratios.
> >>
> >>Sean
> >>
> >>_______________________________________________
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> >>Bioconductor@stat.math.ethz.ch
> >>https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>
> >>
> >>
> >>
> >
> >
> >
>
>
> --
> James W. MacDonald
> 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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