[BioC] Again on heatmap clusters - dChip style, Pearson's distance. Which solutions ?

[Giulio Di Giovanni]

Looking thru the mailing list and the web I found a question by Saurin Jani 
that's exactly my question. But this question was answered in two different 
ways by Michael Watson and Shi Tao.
I was trusting Michael, because Saurin answered that was Ok, but also Shi 
Tao argumentation look convincing...
here the three mails, starting from the bottom...
Wich one has the more appropriate solution ?

Thanks a lot

Giulio

-----------------------------------
Shi, Tao writes:

Here is what dChip manual says:

"The default clustering algorithm of genes is as follows: the distance 
between two genes is
defined as 1 - r where r is the Pearson correlation coefficient between the 
standardized
expression values (make mean 0 and standard deviation 1) of the two genes 
across the samples used.
Two genes with the closest distance are first merged into a super-gene and 
connected by branches
with length representing their distance, and are then excluded for 
subsequent merging events. The
expression values of the newly formed super-gene is the average of 
standardized expression values
of the two genes (centroid-linkage) across samples. Then the next pair of 
genes (super-genes) with
the smallest distance is chosen to merge and the process is repeated n – 1  
times to merge all the
n genes. A similar procedure is used to cluster samples....."

so, to follow that exactly, what you need to do is something like:

row.dist <- as.dist(1 - cor(scale(t(esetSub2X))))
col.dist <- as.dist(1 - cor(scale(esetSub2X)))
heatmap(esetSub2X, Colv=as.dendrogram(hclust(col.dist,
method="centroid")), Rowv=as.dendrogram(hclust(row.dist,
method="centroid")))

===========================================================================================
>Message: 20
>Date: Tue, 16 Nov 2004 09:05:30 -0000
>From: "michael watson (IAH-C)" <michael.watson at bbsrc.ac.uk>
>Subject: RE: [BioC] How can I get Heatmap using dChip
>	clustering..which is	nice& easy to see patterns
>To: <saurin_jani at yahoo.com>,	"Bioconductor Bioconductor"
>	<bioconductor at stat.math.ethz.ch>
>Message-ID:
>	<8975119BCD0AC5419D61A9CF1A923E95E89817 at iahce2knas1.iah.bbsrc.reserved>
>
>Content-Type: text/plain;	charset="us-ascii"
>
>Hi Saurin
>
>I may be wrong, but it looks like your code calculates the euclidean
>distance between rows of 1-cor(), which is itself a distance matrix of
>sorts.  Try:
>
>row.dist <- as.dist(1 - cor(t(esetSub2X)))
>col.dist <- as.dist(1 - cor(esetSub2X))
>heatmap(esetSub2X, Colv=as.dendrogram(hclust(col.dist,
>method="average")), Rowv=as.dendrogram(hclust(row.dist,
>method="average")))
>
>Mick
>
>-----Original Message-----
>From: Saurin Jani [mailto:saurin_jani at yahoo.com] Sent: 15 November 2004 
>23:28
>To: Bioconductor Bioconductor
>Subject: [BioC] How can I get Heatmap using dChip clustering..which is
>nice& easy to see patterns
>
>
>Hi ,
>
>How can I get dChip clustering on heatmap?..which is
>nice & easy to see patterns.
>
>I am using 1- cor(eset)  but somehow its not working I
>am still getting diff. kind of clustering dendrogram.
>
> > d <- dist((1 - cor(esetSub2X)),method =
>"euclidean");
> > dCol <- dist(t((1- cor(esetSub2X))),method =
>"euclidean");
>
> > heatmap(esetSub2X,Colv=
>as.dendrogram(hclust(d,method = "complete")),Rowv =
>NA,col = rbg,cexRow = 1,cexCol = 1);
>
>
>Am I missing something?
>
>Any heatmap clustering  is helpful.
>
>Thank you,
>Saurin
>
>