Hi Matt: I should have replied to this earlier, but I'm monitoring the list in digest mode... My comments below are to Matt, but referring to Robert's last reply. > If they have no probes in common, and were applied to the same RNA > (essentially technical and not biological replicates) then the two > arrays can be combined into essentially one big matrix. I would do > some careful study to make sure that there were not major differences > between the two (for example look at the distribution of expression, > variance within gene across samples, etc). My approach is generally to > ask what things should be the same, and then to compare them. If there > are big differences then you need to figure out how to address them, > but if not then you can just treat it as if you measured all the > features on the mRNA samples, which type of array was used is > irrelevant. This is essentially what I told you on the phone the other day, treat each slide as a unique array and normalize the A set together and then the B set together. All that you really need to worry about is that the A set behaves consistently and the B set behaves consistently. Don't worry if the A and B set look different since they will: they have different genes on them. > I'm not sure I am following the separate linear modeles part. Most > of what anyone does is gene-at-a-time (you could look at the Category > package for an alternative), and so you would fit separate linear > models to genes within arrays and the same between arrays. Now, combine both A and B sets into one big RGList making sure that the A and B slide corresponding to the same biological sample are in the same column of the list. You are running each linear model one gene at a time so I would not worry about separate models for the different arrays. That will happen automatically. If you want a more sophisticated model, we should visit again as that is possible, but I don't think necessary given your experimental design. You have my phone number and can call anytime. The maize project has paid for some of my time and you should take advantage of that. > When you have duplicate probes from what are essentially different > experiments, then I believe you need to think about a random effects > model. Well, they are are actually the same experiment here, and hybridizations should have been randomized across time and slide to avoid bias, but I do agree with Robert's assessment. Thanks!! Dave H -- David A. Henderson, Ph.D. Insightful Corporation 1700 Westlake Avenue North, Suite 500 Seattle, WA 98109-3044 Tel: 206-802-2307 Fax: 206-283-8691 DNADave at Insightful.Com http://www.insightful.com