Hi Peng, 
As long I'm concerned 
about your issue, I guess that you can choose any metric
that you can interpret easily once you have done 
your analysis.
But I think that for clustering microarray genes,
( I guess it's also suggested in the book you mention, which I
don't have right here)
a good solution is to cluster based on the correlation measure,
this because correlation as a straightforward and easily interpretation.
You may also take a look at the R-pkg such as pamr.

Hope it helps

Cheers

A


Anna Freni Sterrantino
Ph.D Student 
Department of Statistics
University of Bologna, Italy
via Belle Arti 41, 40124 BO.




________________________________
Da: Peng Yu <pengyu.ut@gmail.com>
A: bioconductor <bioconductor@stat.math.ethz.ch>
Inviato: Mer 7 ottobre 2009, 5:52:19
Oggetto: [BioC] How to decide which distance metric to use for micoarray data clustering?

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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