[BioC] RMA vs gcRMA on 2 groups of samples

[James W. MacDonald]

Yes but if I am not mistaken, the OP had a situation in which the 
samples were simply different cell or tissue types, rather than 
different batches. I this case I would favor normalizing all together 
rather than doing things in batches.

Best,

Jim


Robert Gentleman wrote:
> 
> Naomi Altman wrote:
>> Dear Bogdan,
>> Any normalization method that uses a set of arrays, reduces the 
>> variability among those arrays.
>>
>> So, if you have 2 sets of arrays and normalize separately, you will 
>> find that the within set variability is smaller than the between set 
>> variability - i.e. you induce significant differential expression 
>> simply by the normalization.  To avoid this effect, when you are 
>> doing differential expression analysis (or sample clustering) you 
>> must either use methods that normalize each array separately (MAS) or 
>> normalize all together.
> 
>   An alternative (and the one that I prefer) is to do separate 
> normalizations, and to then use some sort of batch effect term in the 
> model used to assess differentially expressed genes.
> 
>   Normalization is intended to clean up the relatively minor issues that 
> arise due to slightly different conditions etc. for arrays that are 
> essentially the same.  As far as I can see it is not intended to adjust 
> for batch effects, and in my experience generally does a bad job of 
> that.  Just because you can normalize (or fit any statistical model) 
> does not mean that you should.
> 
>    best wishes
>      Robert
> 
> 
>> --Naomi
>>
>> At 12:01 PM 11/2/2007, Bogdan Tanasa wrote:
>>> Greetings Naomi,
>>>
>>> thanks for reply. To generalize my question: when dealing with 2 sets of
>>> samples, let's say  X1, X2, ...., Xn  and  Y1, Y2, ..., Yn,
>>> I could run the normalization in 2 ways: A. only X(1,n) and only Y(1,n), or
>>> B. both X(1,n),Y(1,n). Are there any a priori statistical
>>> criteria that favors a way or the other ? If I  would take into
>>> consideration biological criteria (the things I am interested in), the
>>> results
>> >from A may sometimes look better than B', or vice versa. Thanks !
>>> Bogdan
>>>
>>>
>>>
>>> On 11/2/07, Naomi Altman <naomi at stat.psu.edu> wrote:
>>>> Dear Bogdan,
>>>> I do not have an opinion on gcRMA versus RMA.  But if you are doing
>>>> differential expression analysis comparing the cell samples with the
>>>> organ samples, you need to normalize
>>>> all the samples together.
>>>>
>>>> --Naomi
>>>>
>>>> At 11:31 AM 11/1/2007, Bogdan Tanasa wrote:
>>>>> Hi folks,
>>>>>
>>>>> I would like to ask for your opinions on the following:
>>>>>
>>>>> I have 60 expression profiles of 60 samples (cells and organs in
>>>>> resting conditions).
>>>>> I normalized these arrays in many ways, including RMA.
>>>>>
>>>>> Considering the biological arguments (cells samples vs organs
>>>>> samples), I am planning to do the normalization separately, on the
>>>>> group of cell samples, and on the group of organ samples.
>>>>>
>>>>> My questions are:
>>>>>
>>>>> - after RMA normalization on separate groups of samples (cells vs
>>>>> organs), the results are different, but are these better ? GO analysis
>>>>> do not display major differences.
>>>>>
>>>>> - would gcRMA work better than RMA ? The majority of opinions in SoCal
>>>>> are pro-RMA.
>>>>>
>>>>> thanks,
>>>>>
>>>>> Bogdan
>>>>>
>>>>> _______________________________________________
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>>>> Naomi S. Altman                                814-865-3791 (voice)
>>>> Associate Professor
>>>> Dept. of Statistics                              814-863-7114 (fax)
>>>> Penn State University                         814-865-1348 (Statistics)
>>>> University Park, PA 16802-2111
>>>>
>>>>
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>>>
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>> Naomi S. Altman                                814-865-3791 (voice)
>> Associate Professor
>> Dept. of Statistics                              814-863-7114 (fax)
>> Penn State University                         814-865-1348 (Statistics)
>> University Park, PA 16802-2111
>>
>> _______________________________________________
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>> Bioconductor at stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
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>>
> 

-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
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