Dear Dave, At 10:45 PM 31/07/2003, Dave Waddell wrote: >You have a couple of spelling mistakes in the page: > ><http: bioinf.wehi.edu.au="" limma="" library="" limma="" html="" 5linearmodels.htm="" l="">http://bioinf.wehi.edu.au/limma/library/limma/html/5linearmodels.htm l > >estime and explanded Thanks for letting me know. These typos have been corrected in later versions of limma. >Can you point me to a place that would more fully explain the design >matrix and contrasts with respect to 2-colour dye experiments? My best suggestion at this time is: Yang, Y. H., and Speed, T. P. (2003). Design and analysis of comparative microarray experiments. In T. P. Speed (ed.), Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall/CRC Press, pages 35-91. But basically limma is breaking new ground here so there are no good references for this stuff apart from the User's Guide itself. I am working on providing more user friendly interfaces to create design and contrast matrices and more documentation, but obviously these things take time. In the meantime, a local statistician would be able to give you some help. Or you could ask for help on bioconductor about specific designs. > In some Bioconductor packages, the design matrix appears to be > applicable to the Cy3/Cy5 experiment as a whole and in others to the > individual Cy3 and Cy5 experiments. I am not clear what you mean here. As far as I know, limma is the only package to have the concept of a design matrix and limma is designed to analyze the whole experiment at once. Other packages basically assume you are making only one comparison usually with replicate arrays. > It is very confusing. In addition, the meaning of a contrasts matrix and > how to put one together is not very clear. Both of these values, if > applied incorrectly, would appear to me (as a non-statistician assigned > to put together a package) to completely change the results. Yes, this is true. > Finally, can you tell me how limma handles control spots? The only explicit handling of control spots in limma is in the plotMA function. I assume that you will leave the control spots in during the normalization (perhaps using weights to downweight ratio controls spots or to upweight MSP titration spots) and you will remove them before doing inference about differential expression. There are subsetting commands to make removing control spots easy. >Thanks for a great package, Dave. Thanks for your comments. Gordon