[BioC] loged data or not loged previous to use normalize.quantile
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
>Thanks a lot
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
Naomi S. Altman 814-865-3791 (voice)
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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