On Wed, Oct 7, 2009 at 10:49 AM, Peng Yu <pengyu.ut@gmail.com> wrote:

> Besides the distance metrics, there are other things that may also be
> important. For example, multiple probesets map to a same gene. I can
> do clustering on probeset values or on averaged probeset values of
> genes. What factors should I consider when I make this kind of
> decisions?
>
>
It is generally best not to average probes.  You could choose one to be
representative of each gene, but averaging is not the best way to go.


> bioDist says something about two popular metrics, but the description
> is distilled. I am wondering whether there are some more detailed
> comparisons between metrics.
>

Often, the metrics produce highly compatible pictures of the data.  The
actual metric you will use may be directed somewhat by the goals of the
analysis but, at least for hierarchical clustering, I think it is difficult
to argue for one "best" or "recommended" metric.

In practice, you may want to try a few to see how they behave on your data.

Sean



>
> On Wed, Oct 7, 2009 at 12:35 AM, Tim Triche <tim.triche@gmail.com> wrote:
> > look at the bioDist package for some suggestions.
> >
> > the metric to use depends on your task.
> >
> >
> > On Tue, Oct 6, 2009 at 8:52 PM, Peng Yu <pengyu.ut@gmail.com> wrote:
> >>
> >> Hi,
> >>
> >> I am looking for the most appropriate distance metrics for the
> >> clustering of a set of microarray data. And I read Chapter 12 of
> >> Bioinformatics and Computational Biology Solutions Using R and
> >> Bioconductor, But I'm still not clear what the general guide line is
> >> to choose an appropriate distance metrics out of many ones list in
> >> that chapter. Could somebody let me know how to choose an appropriate
> >> distance metrics?
> >>
> >> Regards,
> >> Peng
> >>
> >> _______________________________________________
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> >
> >
> >
> > --
> > Statisticians, like artists, have a bad habit of falling in love with
> their
> > models.
> > --George Box
> >
>
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