Hi, I wonder after running fitPLM, besides coeff and standard errors, what else are computed in the procedure? How can I check it? For the image, how can I turn on the legend? The documentation mentions turn on the add.legend which I'm not sure how to do it. And, the command in line -3 pg3 of AffyPLM is cut off, can someone give me the complete command? Thanks. Yen Lin

> For the image, how can I turn on the legend? The documentation mentions > turn on the add.legend which I'm not sure how to do it. Perhaps something like image(Pset,add.legend=TRUE) > And, the command in line -3 pg3 of AffyPLM is cut off, can someone give me > the complete command? Pset <- fitPLM(Dilution,model=PM~-1+probes+logliver+scanner, normalize=FALSE, background=FALSE, variable.type=c(logliver="covariate"))

Lawrence, In my experience of using the images its very easy to see artefacts such as dirt or air bubbles as these show up as green on the images. Recently I also detected a green strip across a certain batch of chips and I'm currently looking into whether there could be a scanner problem. Also a 'bad' chip could appear darker overall. Ideally I guess you're looking for a random distribution of colours on a chip and a similar overall colour between chips. In general chips that look quite bad still seem to give resonable data, I don't know what the point is when you exclude a chip. As for the residuals vs. SE perhaps this poster might help. http://stat- www.berkeley.edu/users/terry/Group/talks/Aug2003/QCPoster.pdf As a secondary point is the NUSE discussed in the poster the same as the model SE that you can boxplot in affyPLM, as the boxplots are adjusted so each probeset has a median of 1? I'd also appreciate a lay-mans explanation of what the output of PLM and what it can be used for. For example can the SE's be used in any way to get some kind of confidence for differential expression? Cheers, Matt