[BioC] loged data or not loged previous to use normalize.quantile
naomi at stat.psu.edu
Tue Apr 5 15:16:16 CEST 2005
I just want to remind people that permutation tests, rank tests, etc still
require i.i.d. errors. So the variance needs to be stabilized even for
At 01:32 PM 4/4/2005, Fangxin Hong wrote:
>As what Wolfgang mentioned, non-parametric permutation test is an option
>when t-distribution assumption is not valid. But if you have few
>replications (2-3), most permutation tests don't have power either. I
>would suggest you try RankProd package, which would be powerful enough to
>detect differentially expressed genes with 2 replications.
> > Hi Marcelo,
> > the difference is that the power of the test you are doing can be
> > different when you consider the data on the "raw" or on the
> > log-transformed scale.
> > Also, the p-value calculated by limma is based on the assumption that
> > the null-distribution of the test statistic is given by a
> > t-distribution; this assumption might be more or less true in both cases.
> > You are really doing two different tests: test A, say, consists of
> > applying the t-statistic to the untransformed intensities, test B, say,
> > applying the t-statistic to the transformed intensities.
> > Then, if you want to use the t-distribution for getting p-values, you
> > need to make sure that the null distribution of your test statistic
> > is indeed (to good enough approximation) t-distributed. You can do this
> > e.g. by permutations. For that you need either a large number of
> > replicates, or to pool variance estimators across genes.
> > If you don't want to make a parametric assumption for getting p-values,
> > you need a larger number of replicates; if you have these, you can for
> > example calculate a permutation p-value.
> > So, there is really no "right" or "wrong" about transforming, or which
> > transformation -- as long as you don't violate the assumptions of the
> > subsequent tests. If the assumptions are met, then the procedure with
> > the highest power is preferable. And that depends very much on your data
> > (about which you have not told us much.)
> > Hope that helps.
> > And here is another shameless plug: have a look at this paper:
> > Differential Expression with the Bioconductor Project
> > http://www.bepress.com/bioconductor/paper7
> > Best wishes
> > Wolfgang
> > 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
> >> Marcelo
> >> _______________________________________________
> >> Bioconductor mailing list
> >> Bioconductor at stat.math.ethz.ch
> >> https://stat.ethz.ch/mailman/listinfo/bioconductor
> > --
> > Best regards
> > Wolfgang
> > -------------------------------------
> > Wolfgang Huber
> > European Bioinformatics Institute
> > European Molecular Biology Laboratory
> > Cambridge CB10 1SD
> > England
> > Phone: +44 1223 494642
> > Fax: +44 1223 494486
> > Http: www.ebi.ac.uk/huber
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor at stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
>Fangxin Hong, Ph.D.
>Plant Biology Laboratory
>The Salk Institute
>10010 N. Torrey Pines Rd.
>La Jolla, CA 92037
>E-mail: fhong at salk.edu
>Bioconductor mailing list
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