[BioC] Bimodal Distrinbution
c.mayer at abdn.ac.uk
Wed Dec 10 16:44:35 CET 2008
You are not very specific about what you mean by bimodal distribution, but I assume that you mean the distribution across ALL proteins. This would suggest that you can roughly classify your measurements into two groups: small ones (mode1) and large ones (mode2). It wouldn't have direct implications though if you want to find differentially expressed proteins, because there you only compare the values for the same protein.
So for example the aim of a normalization would not be to remove the bi-modality but to make sure that the bi-modal distribution is more or less the same for each sample (at least for the non-changing proteins).
From: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Francesco Mancuso
Sent: 09 December 2008 18:19
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] Bimodal Distrinbution
I'm a little newbie with R...
I'm working with quantitative proteomics data that have a bimodal
For you what is the best function to work with this type of data?
Thanks in advance!
*Francesco Mattia Mancuso*
/Proteomics and Functional Genomics Group/
/Mass Spectrometry Unit/
European Institute of Oncology
Via Adamello 16 - 20139 Milano
[email] francesco.mancuso at ifom-ieo-campus.it
<mailto:francesco.mancuso at ifom-ieo-campus.it>
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