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 >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> >> -- >> James W. MacDonald, M.S. >> Biostatistician >> University of Washington >> Environmental and Occupational Health Sciences >> 4225 Roosevelt Way NE, # 100 >> Seattle WA 98105-6099 >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > Matthew N McCall, PhD > 112 Arvine Heights > Rochester, NY 14611 > Cell: 202-222-5880