[BioC] normalizing only 2 affy samples

[Matthew McCall]

If your affy arrays are one of the supported platforms (hgu133a,
hug133plus2, mouse4302, or exon st), you might also consider using
frma. This allows you to preprocess individual arrays and has the
advantages of rma over mas5.

Best,
Matt

On Mon, Apr 9, 2012 at 10:49 AM, James W. MacDonald <jmacdon at uw.edu> wrote:
> Hi Juliet,
>
> On 4/9/2012 9:55 AM, Juliet Hannah wrote:
>>
>> All,
>>
>> Can anyone suggest a strategy to normalize just two affy samples?
>>
>> I do not seek to carry out any inferential procedures. I would just
>> like to make a scatter plot
>> of the expression values from both arrays just to see if the
>> experiment worked (that is
>> expression is being measured).
>
>
> When you say 'normalize' do you really mean normalize, or are you using that
> term in the context of normalization and summarization, in order to get
> probeset-level expression values?
>
> I'll assume for sake of argument that you mean normalization and
> summarization.
>
> With only two arrays, it isn't clear what the best course of action should
> be. You could argue that mas5() is a better idea, as the model being fit is
> probably the simplest, and is more likely to have assumptions fulfilled. The
> downside to that approach is that mas5() really isn't very good.
>
> The summarization method in rma() fits a much more complex model, and given
> only two samples, you could argue that the estimates for probe and chip
> effects won't be very stable.
>
> So either method has inherent drawbacks with so few samples. I would tend to
> use rma() anyway, but that is my bias and is partially dictated by a long
> history of using rma(), and a desire for consistency. I actually doubt there
> will be that much difference in the end.
>
> You might also consider using an MA plot rather than a scatter plot for
> visualization. It will tend to be more interpretable and easier to see what
> is going on.
>
> Best,
>
> Jim
>
>
>>
>> Thanks,
>>
>> Juliet
>>
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>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
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>
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