Hi, here I come again about normalisation... :) I have a set of 4 cDNA 2-colour arrays, including dye-swap. 00 & 01 are in one direction, 02 and 03 in the opposite. Each hybridisation corresponds to untreated sample vs. transfected sample (experiments on cell lines). The treatment appears to affect MANY genes. I plotted the raw data log2(ratio) vs log2(product)... and this is what i get: http://mcnach.com/MISC/raw_RI_plots_M1.png No background correction was applied. The print-tip loess curves are shown here for the first slide (00): http://mcnach.com/MISC/print-tip_loess_plot_for_slide_140000_M1.png By the nature of this experiment (transfection of a fusion gene, made up of a DNA-binding section with unknown specificity, but we expect it'll pick up a lot of genes, and a transactivator domain) we are not surprised to find many genes are activated. These are mostly the ones I want, not so much the ones that have enhanced expression, but the ones that go from not detectable expression to detectable levels. The plots show there is a strong dependence between log2(ratio) and intensity, mostly on the left... the higher intensity spots show a less strong dependency. This is also seen nicely when looking at the loess curves. Now, I feel that a lot of the genes I might be interested in will probably be in teh first half, since I'm looking at genes where the signal before transfection is just about background... and it's this region where the curves are steeper. If I apply loess, I'll flatten the whole thing. In fact, it looks like this: http://mcnach.com/MISC/MAplots2.png this image is from a different experiment (loess normalised, and processed with limma), but the plot looks pretty much the same, and you get that higher density diagonal (around 45 degrees) where I find that these genes that are activated by the treatment tend to cluster. So... on the one hand, I feel I may not be doing the best sort of normalisation, yet I do get meaningful results that verify okay by RT, so it's not all too bad. And I have no idea what other method would be better suited anyway... I have no set of known invariant control genes I could use, which I guess would be the best... although I might be able to figure something out in the future. What is the feeling of more experienced people here? Have you worked with experiments like this? Am I worrying too much just to try to get a few % more reliable data? Any comments from anyone greatly appreciated! Jose -- Dr. Jose I. de las Heras Email: J.delasHeras at ed.ac.uk The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374 Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360 Swann Building, Mayfield Road University of Edinburgh Edinburgh EH9 3JR UK