[BioC] loged data or not loged previous to use normalize.quantile

Naomi Altman naomi at stat.psu.edu
Sun Apr 3 18:56:52 CEST 2005

The reason we take log is that there is some evidence that the variance of 
intensity increases with intensity.  This messes up statistical methods 
such as t-tests, ANOVA, limma, rank tests like Wilcoxon and permutation 
tests SAM, which assume that the variance of a single gene does not depend 
on the expression level of the gene.  Taking log removes the dependence of 
variance on the level when the variance increases quadratically with the 

If you do an MA plot of the log data, you will usually observe that on the 
log scale, the variance is higher for low intensity genes.  This indicates 
that taking logarithms overcorrects.  While a couple of fixes have been 
suggested (e.g. Churchill's work and MAANOVA ) these use transformations 
that are not as readily understood as logarithms.

All in all, I would say analysis based on the log data is more reliable 
than analysis based on the raw data.  If all we were interested in were 
tests (and not e.g. estimates of fold difference) I would probably use 
another variance stabilizing method - but this has not yet proved to be the 
case with the biologists I work with.


At 02:20 PM 4/1/2005, 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
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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