[BioC] Agilent single color array

[Kasper Daniel Hansen]

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