[BioC] is-normalisation-really-required

Gordon Barr gbarr at hunter.cuny.edu
Wed Apr 13 15:59:37 CEST 2005

Gorjanc and Vijay

This is a misconception as to why to normalize the data. It is not so  
that we can get "pleasing" results or agreement between analytic  
methods but because statistically it is the correct thing to do. If I  
use the wrong statistical test on a set of data (e.g. parametric tests  
on data  that violates all the assumptions) and it gives the same  
result as an appropriate non-parametric analysis that does not make it  
"right" and ok to do again. It means I got lucky. If the analysis of  
non-normalized data is the same as of normalized data you are lucky not  
right. Sean is on target- if they agree normalize; if they do not agree  
normalize. I would add to that why bother analyzing the non-normalized  


Gordon A. Barr, Ph.D.
Senior Research Scientist
NYS Psychiatric Institute
Columbia College of Physicians and Surgeons
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On Apr 13, 2005, at 8:29 AM, Gorjanc Gregor wrote:

>> -----Original Message-----
>> From: Sean Davis [mailto:sdavis2 at mail.nih.gov]
>> Sent: sre 2005-04-13 14:16
>> To: Gorjanc Gregor
>> Cc: bioconductor at stat.math.ethz.ch
>> Subject: Re: [BioC] is-normalisation-really-required
>> On Apr 13, 2005, at 8:03 AM, Gorjanc Gregor wrote:
>>> Hi!
>>> You might try analysis with and without normalization and take
>>> a look at the results. If they say the same thing than I would
>>> say, no it is not necessary to do normalization.
>> So, if the two results agree, then the results with normalization are
>> correct; if not then the results with normalization are still correct.
>> Sounds like we are pretty much stuck with normalization....
>> Sean
> Why should one do normalization if the results aren't different. But,  
> in
> that case it really does not matter and one can do it or not.
>>> dear friends
>>> i have situation, where i thought its ok for me not to
>>> do normalisation, i am afraid i may be wrong. i want
>>> your advice in this regard.
>>> we performed a wild type - mutant, dye-swap
>>> experiment.
>>> when we analysed the intensity values, they were
>>> consistant among the two experiment (dye-swap). ie.,
>>> almost same values for mutants in both the experiments
>>> of the dye-swap.
>>> since the values are almost same, i thought there
>>> might not be any dye-bias, so i just went ahead,
>>> averaged the two values, found out their ratio and
>>> filtered genes with 2 fold change.
>>> so i have done this without normalisation.
>>> i am afraid, i might be wrong, my 2 fold chaging genes
>>> might be wrong...
>>> kindly give me your advice in this regard.
>>> i did analyse the data with limma, but the topTable
>>> genes there never correlates with my 2 fold genes.
>>> kindly correct me.
>>> thanks
>>> vijay
>>> graduate student
>>> department of biological sciences
>>> the university of southern mississippi
>>> MS, USA
>> --
>> Lep pozdrav / With regards,
>>     Gregor Gorjanc
>> ---------------------------------------------------------------------- 
>> -
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