[BioC] Agilent single color array

[Mark Cowley]

Hi Kasper,
Do you know what Feature Extractor does to create gProcessedSignal. Is  
there any obvious reason why the data seems worse?

cheers,
Mark
-----------------------------------------------------
Mark Cowley, BSc (Bioinformatics)(Hons)

Peter Wills Bioinformatics Centre
Garvan Institute of Medical Research
-----------------------------------------------------

On 01/07/2008, at 4:48 AM, Kasper Daniel Hansen wrote:

>
> On Jun 30, 2008, at 11:01 AM, Sean Davis wrote:
>
>> On Mon, Jun 30, 2008 at 1:54 PM, Kasper Daniel Hansen
>> <khansen at stat.berkeley.edu> wrote:
>>> Hi
>>>
>>> I have gotten my hands on data from the single color Agilent  
>>> platform using
>>> a custom array design and I would like to hear what people are  
>>> usually doing
>>> when it comes to preprocessing.
>>>
>>> I have previously analyzed some two color arrays from Agilent and  
>>> found that
>>> the data I had was pretty standard when it comes to normalization.  
>>> Even
>>> though I preferred doing my own preprocessing the Agilent supplied
>>> gProcessedSignal and rProcessedSignal columns were decent (this  
>>> was from a
>>> much earlier version of their software - Feature Extractor).
>>>
>>> But for the one color arrays I find that gProcessedSignal performs  
>>> horrible
>>> - flat out horrible, the raw data looks much better. Furthermore,  
>>> when I
>>> normalize between I arrays I see relatively little effect of  
>>> normalization,
>>> sometimes the normalization even increases the spread on MA plots  
>>> where I
>>> would not expect it to do anything. Of course this may be related  
>>> to the
>>> hybridizations done or the array design I have in hand, but I  
>>> still find it
>>> somewhat surprising.
>>>
>>> I have tried vsn2 from vsn, quantile normalization and quantile
>>> normalization following normexp (offset 25 and 50) background  
>>> correction
>>> from Limma. All 3 (4 if you count the 2 offsets) combinations have  
>>> also been
>>> done with and without subtracting the local background estimate  
>>> from Feature
>>> Extractor (the gBGMeanSignal column).
>>>
>>> Anyway, I am curious as to what other people's experience using this
>>> platform are.
>>
>> What type of array is it?  In particular, is it miRNA?
>
> No, it is a custom splice junction design using (I believe) a  
> standard mRNA protocol (expect that we are hybing at 70 degrees  
> instead of 65 degrees based on some assessment). We are doing a  
> pilot study so we do not have too much experience with this platform.
>
> But the raw data looks very nice and interpretable - it is more the  
> fact that normalization seems to have little effect (we can always  
> argue about how much I want to normalize - but that is not really my  
> concern here) coupled with the fact that the processed signal looks  
> crappy based on comparing two replicate arrays. I am not really  
> saying that the platform sucks, in fact one could interpret the fact  
> that normalization have little effect to mean that the raw data is  
> super good. I am just wondering what other peoples experience is.
>
> The only normalization that really seems to have an (big) effect is  
> normexp with an offset of 50, which certainly shrinks the M values  
> towards zero.
>
> Kasper
>
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