[BioC] Agilent single color array
Kasper Daniel Hansen
khansen at stat.Berkeley.EDU
Tue Jul 1 01:16:42 CEST 2008
There is a long explanation in their manual which I have not really
bothered to read very carefully.
For the two color arrays it was essentially a variation on loess
normalization together with an attempt at dealing with saturated
probes and probes that were not present.
For the single color array I find it hard to understand what exactly
they _could_ do. The measure is computed without using other arrays so
they just have a single channel. This is different from the two color
situation where at least you have competitive hybridization. It sort
of corresponds to doing something to affy data with a single array and
only one probe in each probeset. Anyway, so it is pretty obvious that
they don't have the same amount of data that they do with the two
Anyway, as I said I am mostly interested in hearing with other
people's experiences are. I am not too worried about making my own
conclusions - I do have some experience at analyzing microarrays, but
I am curious (since this is a pilot study) as to whether these
"issues" are present for other people.
On Jun 30, 2008, at 3:40 PM, Mark Cowley wrote:
> Hi Kasper,
> Do you know what Feature Extractor does to create gProcessedSignal.
> Is there any obvious reason why the data seems worse?
> 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:
>>>> 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
>>>> 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
>>>> 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
>>>> 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.
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