Dear Wolfgang,

Thank you for your advices. I did'nt find any spatial heterogeneity on 
the array which could explain a bi-modal distribution of intensity. One 
possibility is that we have two populations of probes corresponding to 
the two genomes of tobacco. But it is difficult to check.
I tried different methods of global normalization, which seem to give 
approximately the same results. So, I wonder what kind of criteria I 
could use to select the "better" method ?

Christian


Wolfgang Huber a écrit :
> Dear Christian
>
> the common normalisation methods assume that the normalisation between 
> arrays involve one common transformation (linear in the case of median 
> scaling, affine linear in the case of vsn with default parameter 
> 'calib', local polynomial in the case of loess, non-parametric rank 
> based in the case of quantile normalisation). I listed these in some 
> order of flexibility.
>
> However, if you have two distinct populations of intensities, I would 
> recommend first finding out the origin for those - try to look for 
> associations of these two  populations with all sorts of feature 
> parameters (annotation, spatial position on the array) and make sure 
> there is no show-stopper quality problem. Once you have that, the 
> proper normalisation (perhaps stratified)  will follow from that.
>
> Just applying one single overall transformation might be OK, but it 
> could lead to distortions and inefficiency.
>
>  Best wishes
>     Wolfgang
>
>
>
>
> Christian Brière wrote:
>> Yes, I removed Agilent controls and I filtered the data using Agilent 
>> flags (IsPosandSignificant and IsWellAboveBackground) before 
>> calculating intensity distribution.
>> The array was designed using Tobacco contigs defined from available 
>> tobacco ESTs.
>> My question is: whatever the origin of this bi-modal distribution, is 
>> it a problem for normalization and what kind of normalization is the 
>> most adequate ?
>>
>> Christian
>>
>> Sean Davis a Ã©crit :
>>>
>>> On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi@stat.psu.edu 
>>> <mailto:naomi@stat.psu.edu>> wrote:
>>>
>>>     Did you remove the Agilent controls before looking at the
>>>     intensity distribution?
>>>
>>>
>>> And are these some odd array design like tiling arrays?
>>>  
>>>
>>>
>>>     At 06:01 AM 2/3/2009, Christian BriÃ¨re wrote:
>>>
>>>         Hi!
>>>
>>>         I am new in microarray analysis and in using Bioconductor. I
>>>         need to
>>>         analyse expression data from monocolor Agilent microarrays
>>>         (105K).  To
>>>         my surprise, for each array (controls as well as treated
>>>         samples) the
>>>         distribution of intensity data is bi-modal. Furthermore, it
>>>         seems that
>>>         more than 10% of the genes are differentially expressed
>>>         between controls
>>>         and treated samples. Therefore, I wonder what is the best
>>>         method to use
>>>         in such case for between arrays normalization. I was told that
>>>         median or
>>>         quantile normalization was not adequate. Should  Invariant Set
>>>         or VSN
>>>         normalization be better, and what are the packages to use for
>>>         that ?
>>>         Thanks for your help
>>>
>>>         --
>>>
>>>         Christian BriÃ¨re
>>>         UMR CNRS-UPS 5546
>>>         BP42617 Auzeville
>>>         F-31326 Castanet-Tolosan (France)
>>>         tel: +33(0)5 62 19 35 90
>>>         Fax: +33(0)5 62 19 35 02
>>>         E-mail: briere@scsv.ups-tlse.fr
>>>         <mailto:briere@scsv.ups-tlse.fr>
>>>         <mailto:briere@scsv.ups-tlse.fr 
>>> <mailto:briere@scsv.ups-tlse.fr>>
>>>
>>>         http://www.scsv.ups-tlse.fr
>>>         http://www.gdr2688.ups-tlse.fr
>>>         <http://www.gdr2688.ups-tlse.fr/index.php>
>>>         http://www.ifr40.cnrs.fr
>>>
>>>
>>>
>


-- 

Christian Brière
UMR CNRS-UPS 5546
BP42617 Auzeville
F-31326 Castanet-Tolosan (France)
tel: +33(0)5 62 19 35 90
Fax: +33(0)5 62 19 35 02
E-mail: briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-tlse.fr>

http://www.scsv.ups-tlse.fr
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