[BioC] normalizing only 2 affy samples

[Juliet Hannah]

Jim/Matt,

Thanks for the responses. Yes, my language was not precise. As you
suspected Jim, I meant
some way to obtain probeset level measures to make an MA plot or scatter plot.

Matt, I did have frma in mind as well. Thanks for your work on that.

Regards,

Juliet

On Mon, Apr 9, 2012 at 11:18 AM, Matthew McCall <mccallm at gmail.com> wrote:
> 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
>> 4225 Roosevelt Way NE, # 100
>> Seattle WA 98105-6099
>>
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>
>
>
> --
> Matthew N McCall, PhD
> 112 Arvine Heights
> Rochester, NY 14611
> Cell: 202-222-5880