[BioC] About weight function for Agilent data

[Nataliya Yeremenko]

Gordon K Smyth wrote:

>>Date: Thu, 17 Nov 2005 22:10:33 +0100
>>From: Nataliya Yeremenko <eremenko at science.uva.nl>
>>Subject: [BioC] About weight function for Agilent data
>>To: Bioconductor List <bioconductor at stat.math.ethz.ch>
>>
>>I have seen here in the BioC topics several threads concerning weighting of
>>the Agilent data, howeever I didn't understand how important it is,
>>how does it influence linear models and differential expression testing.
>>
>>In particular Agilent Feature extraction performs quite a lot of
>>flagging and do normalization itself.
>>What kind of flags is important to set for weight zero?
>>Should control spots be weighted zero as well?
>>Is it wise to use processed intensities (and do not use withinarray
>>normalisation of Limma) instead of raw data?
>>There are number of between normalisations, but which one to use?
>>
>>
>>--
>>Dr. Nataliya Yeremenko
>>
>>Universiteit van Amsterdam
>>Faculty of Science
>>IBED/AMB (Aquatische Microbiologie)
>>Nieuwe Achtergracht 127
>>NL-1018WS Amsterdam
>>the Netherlands
>>
>>tel. + 31 20 5257089
>>fax  + 31 20 5257064
>>    
>>
>
>  
>
Thanks Gordon for explanation.

>Control spots should be removed before the differential expression analysis.
>  
>
As far as I understood control spots should be removed by assigning zero 
weight in read.maimages step
(by creating wt.fun).
Is it correct?

>Apart from that, my experience is that most flags estimated by image analysis programs are best
>ignored.  They tend to be very conservative and to encourage you to remove data which is actually
>quite usuable in the context of a replicated experiment.  However this is not based on any careful
>analysis of AgilentFE's flags, so you may find differently.
>  
>
There are 8 flags in FE:  they cover Feature and background 
non-uniformity and population outliers in each channel separately.
Which one are important to down-weight before normalization?

Concerning normalization step:
As for within normalization Is seems that only "lowess" is suitable for 
Agilent arrays.
How to estimate what is most reliable betweennormalization ?
For example "Aquantile" produced plotddensities much more fitted to each 
other than  "vsn".
Does it mean that "vsn" is not good in my case?

>If you are using limma's lmscFit function, you do not have the option of weighting or flagging
>spots anyway.
>
>  
>
But I'm still using weighting for the normalization step.
I have used splitting two-colour arrays into two single-channel ones,
just only because appart from biological replicates I have as well
some technical one, where biological replicates are mixed:
array1:  A1 vs O1
array2:  O2 vs A2
array3:  A2 vs O3
etc...
(almost each one, but not all of them, are technicaly replicated by 
dye-swap as well)
I understood now that this kind of design is far from ideal, but 
experiments are done.
Is there still way to create design for that type of experiment and 
contrast A vs O?
There is only one way to create such a contrast with use of all 
replicates is to perform singel channel fitting.
Is that true?

Regards
Nataliya

>Best wishes
>Gordon
>
>  
>
-- 
Dr. Nataliya Yeremenko 

Universiteit van Amsterdam
Faculty of Science
IBED/AMB (Aquatische Microbiologie)
Nieuwe Achtergracht 127
NL-1018WS Amsterdam
the Netherlands

tel. + 31 20 5257089
fax  + 31 20 5257064