RESENDING WITHOUT ATTACHMENT:
 
Scaling is well known to cause different hierarchical (and non-hierarchical) clustering results.  The decision to transform the data has to be considered in terms of how the transformation will impact the distance calculations.  We are very comfortable with transforming to induce things such as normality or homoscedasticty, however, this is not why we would necessarily do it in a clustering problem. 
 
I have attached a review article (in a previous post) on clustering microarray data that shows a simple example of how scaling results in different clusters, and why one would be used over the other.  (Pharmacogenomics, 2003, Vol 4(1), pps. 41-52).
 
Bill Shannon
Associate Professor of Biostatistics in Medicine
Washington University School of Medicine
St Louis
 President-Elect, Classification Society




--- On Fri, 8/29/08, Deanne Taylor <dtaylor@hsph.harvard.edu> wrote:

From: Deanne Taylor <dtaylor@hsph.harvard.edu>
Subject: Re: [BioC] Hierarchical clustering of RMA data
To: bioconductor@stat.math.ethz.ch, rkirkbride@ucdavis.edu
Date: Friday, August 29, 2008, 6:35 AM

Ryan:

This might be a naive question as I'm not sure how dChip is doing the
normalization, but is there a setting in dChip to let it know it's a log2
scale? Otherwise the mathematics between log and linear scale would be much
different... and that might be the source of the difference, as subtracting log2
data is akin to dividing at the linear scale.



---
Deanne Taylor PhD
Executive Director, Bioinformatics Core
Department of Biostatistics
Harvard School of Public Health
655 Huntington Avenue
Boston, MA 02115
dtaylor@hsph.harvard.edu






>>> Ryan Kirkbride <rkirkbride@ucdavis.edu> 08/28/08 8:27 PM
>>>
Hello all!

I have a basic conceptual question:

I have a set of RMA normalized data that I am looking to carry out  
hierarchical clustering.
In the past we've usually been working with MAS5 data which we import  
into dCHIP to carry out the clustering.
I'm now looking to do the same with RMA data, and I'm wondering if I  
should transform to a linear scale or leave it the typical log2 scale.
dChip does a per gene normalization (subtracts the mean and then  
divides by the standard deviation), and it appears that linear or log2  
scale affects the results.

I'm assuming most people just leave it log2 scale, am I overthinking  
the whole issue?

Thanks,


_________________________
Ryan Kirkbride
Plant Biology Graduate Student
Harada Lab
UC Davis





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