[BioC] loged data or not loged previous to use normalize.quantile
Kasper Daniel Hansen
k.hansen at biostat.ku.dk
Fri Apr 1 22:53:31 CEST 2005
Almost any statistical analysis you do will be impacted by transforming
the data. This is to be expected, so this is something you generally
want to consider before (and during) your analysis.
In the microarray litterature there are zillions of examples showing
that it is (usually) preferable to do your analysis on the log2 scale.
One reason is that you are generally looking for relative changes
instead of absolute changes, but there are more.
On Fri, Apr 01, 2005 at 03:20:17PM -0300, Marcelo Luiz de Laia wrote:
> Dear Bioconductors Friends,
> I have a question that I dont found answer for it. Please, if you have a
> paper/article that explain it, please, tell me.
> I normalize our data using normalize.quantile function.
> If I previous transform our intensities (single channel) in log2, I dont
> get differentially genes in limma.
> But, if I dont transform our data, I get some genes with p.value around
> 0.0001, thats is great!
> Of course, when I transform the intensities data to log2, I get some NA.
> Why are there this difference? Am I wrong in does an analysis with not
> loged data?
> Thanks a lot
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> Bioconductor at stat.math.ethz.ch
Kasper Daniel Hansen, Research Assistant
Department of Biostatistics, University of Copenhagen
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