[BioC] Aquantile normalization & transcriptional activity

Federico Scossa fscossa at pw.usda.gov
Sat Mar 12 21:18:16 CET 2005

yes, the same amount of RNA has been retrotranscibed and applied to all 
arrays in my experiment. is it still ok to use quantile normalization in 
this case?

thank you for all your help,

>is the same amount of mRNA applied to the chip in all cases? if so, it 
>seems global suppression of mRNA would be compensated for by the use of 
>more cells, i.e. that normlization would still be OK.
>----- Original Message ----- From: "Gordon Smyth" <smyth at wehi.edu.au>
>To: "Federico Scossa" <fscossa at pw.usda.gov>
>Cc: <bioconductor at stat.math.ethz.ch>
>Sent: Saturday, March 12, 2005 6:14 AM
>Subject: [BioC] Aquantile normalization & transcriptional activity
>>>Date: Fri, 11 Mar 2005 18:41:52 -0800
>>>From: Federico Scossa <fscossa at pw.usda.gov>
>>>Subject: [BioC] Aquantile normalization & transcriptional activity
>>>To: fscossa at pw.usda.gov
>>>Cc: bioconductor at stat.math.ethz.ch
>>>Hi all,
>>>I have a concern about using Aquantile normalization (limma) in my
>>>experiment. I hope someone can help me and clarify the issue.... I have
>>>several timepoints... in particular one of them, as expected, shows a
>>>global, low trascriptional activity (the tissue at this timepoint is close
>>>to the "drying stage", so most of the genes are not expressed)...
>>>so if I apply Aquantile normalization I am going to modify the channel
>>>densities, so that they can overlap. and this is necessary, as far as I
>>>understand, because it makes the between-timepoints comparisons possible
>>>(my design is unconnected). but, in this way, am I introducing some
>>>artifacts in my low-expression timepoints? I mean, I am forcing  the
>>>channel intensities to have all the same distribution... but which is the
>>>assumption of Aquantile ? should the single channel intensities be roughly
>>>the same before normalization?
>>You are correct in your suspicion. If one of your samples is expected to 
>>show systematically lower expression over the whole genome, then the 
>>basic assumptions behind quantile normalization is invalidated. Your 
>>options in such a situation are limited. Depending on what is printed on 
>>your arrays, you could normalize on a subset of control spots which 
>>should have nearly constant expression. You are going to need some sort 
>>of boutique normalization.
>>Bioconductor mailing list
>>Bioconductor at stat.math.ethz.ch

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