I tend to use a 'consensus' approach when doing cluster analysis.  If by linkage distance you mean genetic linkage (I assume you do), you could try the various linkage distances and see if the dendrogram is stable.  This also works if you are dealing with non-genetic distance measures.

If you do this and the dendrograms are essentially stable you are done.  More formal methods of consensus trees (dendrograms) can be found doing a search on work by Fred McMorris (look in discrete math and evolutionary biology) and the numerical taxonomy software PAUP I believe has consensus methods in it.

Maybe Tom Girke has consensus tools in R/Bioconductor.

Bill Shannon
Washington Univ. School of Medicine

PS -- I am running for President elect of the Classification Society of North America and encourage anyone doing cluster/classification work to look at this society for their research and publications (Journal of Classification and http://www.classification-society.org/csna/csna.html)



Daniel Brewer <daniel.brewer@icr.ac.uk> wrote: Hi,

I have been producing some dendograms using hclust with a variety of
linkage distance measures.  Does anyone know or is there a good resource
that explains why one would use one linkage distance rather than another?

I don't really like dealing with dendograms, but we want to produce
groupings based on these to do differential analysis on, and I would
like to be able to justify it.

Thanks

Dan

-- 
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Daniel Brewer, Ph.D.
Institute of Cancer Research
Email: daniel.brewer@icr.ac.uk
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