hclust can use any distance metric.

1-correlation is one of several metrics you could use.

1-correlation focuses on the "up and down" behavior, but scales each 
gene to have the same standard deviation.
Euclidean distance focuses more on the overall level of expression.
Euclidean distance with the mean or median removed focuses on the "up 
and down" behavior, but also considers the magnitude of that behavior.


There are many other choices of metric and clustering method.  I 
don't think you can really state that one method or metric produces 
results that are more
"biologically meaningful".  I think that depends on what you mean by 
"biologically meaningful".

If you have replicate arrays for the same biological condition, these 
should be averaged before clustering, as you want to cluster based on 
the response to the
condition, not on the noise.

The paper below is very readable and sheds a lot of light on these issues

<http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WK9-4CB0HCH-1-1&_cdi=6901&_user=209810&_orig=search&_coverDate=07/31/2004&_sk=999099998&view=c&wchp=dGLbVtb-zSkWz&md5=6cda9b21f97456578db16db820339ae1&ie=/sdarticle.pdf>Problems 
in gene clustering based on gene expression data Journal of 
Multivariate Analysis 90 (2004) 44-66 Jenny Bryan

--Naomi

At 09:58 AM 2/20/2006, Kimpel, Mark William wrote:
>Sean,
>
>Thank you for the reply. Would you be able to provide a brief code chunk
>for the 1-correlation function you describe?
>
>Also, would anyone like to comment on the more bioinformatic slant of my
>question, i.e. do you gain more knowledge about the system by clustering
>using 1-correlation or by hclust? As a biologist, it seems to me that we
>are more often interested in finding genes that behave similarly between
>samples rather than genes with similar mean expression.
>
>If this is true, I wonder if the developers of GOClust could comment on
>the clustering algorithms included as options in their package, which
>are Clara, Hclust, Kmeans, and Pam. Again, as a biologist, I believe
>that GO clustering would most be most appropriately done on genes that
>behave similarly between samples, rather than have similar mean
>expression. If, for example, two genes are exactly inversely
>proportional, then they should cluster right next to each other as they
>may be co-regulated.
>
>I feel fairly confident in my assertions as a biologist, but I am not a
>mathematician, and, if I am misunderstanding how clustering works under
>these various algorithms, please correct me.
>
>Thanks,
>
>Mark
>
>
>
>Mark W. Kimpel, M.D.
>-----Original Message-----
>From: Sean Davis [mailto:sdavis2@mail.nih.gov]
>Sent: Monday, February 20, 2006 8:04 AM
>To: Kimpel, Mark William; Bioconductor
>Subject: Re: [BioC] clustering question
>
>
>
>
>On 2/19/06 23:23, "Kimpel, Mark William" <mkimpel@iupui.edu> wrote:
>
> > I have a general question about clustering of genomic data. The
>heatmaps
> > that are generated are usually scaled row-wise so that variations are
> > apparent within rows but not between rows. In looking at the
> > documentation of heatmap and hclust, however, is appears that this
> > scaling is done after the actual clustering is performed. If heatmap
>is
> > performed on the hclust object with scale="none", it is apparent that
> > most of the row clustering is based on overall gene expression levels,
> > not on similar column-wise behavior between rows.
> >
> > Wouldn't it make sense to scale row-wise before clustering so that the
> > row clusters are based more on the correlation of the behavior of rows
> > between columns, i.e. two genes would be near each other if the genes
> > behaved similarly across samples? I realize that some of this effect
>may
> > be achieved with unscaled data, but it seems to me that the large
> > overall expression differences may minimize that.
>
>Mark,
>
>If I understand you correctly, you might want to look at the "distfun"
>argument to heatmap. The distfun argument allows you to use any
>dissimilarity function that you like, including 1-correlation if you
>like.
>
>Sean
>
>_______________________________________________
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>Bioconductor@stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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