[BioC] Hierarchical clustering of RMA data

Deanne Taylor dtaylor at hsph.harvard.edu
Fri Aug 29 13:35:01 CEST 2008


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 at hsph.harvard.edu

>>> Ryan Kirkbride <rkirkbride at 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?


Ryan Kirkbride
Plant Biology Graduate Student
Harada Lab
UC Davis

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