Good Afternoon, I have been analyzing something like 150 arrays worth of data using LimmaGUI, but one thing I am finding as a biologist hard to get a grip of is the comparability of the B statistic numbers generated. Is a value of 6 for a gene in one experiment directly comparable to a value of 10 for the same gene in a different experiment? One other question I had was on a log odds plot is the log fold change a log based 10 measures so that a change of log fold change 1 would be 10 in reality? Is this the same as the M value given in the top table lists? We are also working with some more complex experimental designs, and I was wondering if anyone had a paper explaining how the linear model is used for different experimental designs in particular a saturated design. Does the model utilize each channel independently taking into account that they are related? In other words will data be taking from only the arrays with direct comparison for the treatments of interest or does it take all of the related labelled sample data independent of the on array comparison? In which case, do you think is reasonable to add arrays used with different designs for analysis that contain the same RNA used for labelling? One final question I have for you all is can the linear model fitting be used with samples/treatments that seem to have global gene regulation effects? Thank you all for your help in advance. Hope these are not ridiculous questions. Yours Sincerely, Liz Brooke-Powell Molteno Building Department of Pathology University of Cambridge Tennis Court Road mbridge, CB2 1QP United Kingdom Website: http://www.path.cam.ac.uk/~toxo/ Tel 01223 33 33 31(office) or 01223 33 33 29 (lab) [[alternative HTML version deleted]]