Question: Again on heatmap clusters - dChip style, Pearson's distance. Which solutions ?

0

Giulio Di Giovanni •

**540**wrote: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
>
>

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modified 13.3 years ago
by
Adaikalavan Ramasamy •

**1.8k**• written 13.3 years ago by Giulio Di Giovanni •**540**