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

Fangxin Hong fhong at salk.edu
Mon Apr 4 19:32:29 CEST 2005

Hi Marcelo;
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
>> _______________________________________________
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> --
> 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
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> 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

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