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Question: Hierarchical clustering of RMA data
0
10.5 years ago by
Ryan Kirkbride10 wrote:
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 [[alternative HTML version deleted]]
normalization clustering • 453 views
modified 10.5 years ago by Deanne Taylor50 • written 10.5 years ago by Ryan Kirkbride10
Answer: Hierarchical clustering of RMA data
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10.5 years ago by
Deanne Taylor50 wrote:
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 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? Thanks, _________________________ Ryan Kirkbride Plant Biology Graduate Student Harada Lab UC Davis [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor