I do not think this is a research question any more. If you need to compare with the reference sample, then dye-swaps are necessary. If the reference sample is used only for normalization, then it is better NOT to dye-swap, since you lose information (i.e. 1 d.f.) from having a dye-effect. Loop designs are more efficient, but more complicated to analyze. They have dye-swap built into the loop (so it is not necessary to run the loop in both directions, as some investigators are doing). --Naomi Altman p.s. I also strongly suspect that if you do dye-swaps, you should do it on biological replicates. (Is there any evidence for biological rep x dye interaction?) If the limiting factor is arrays (not RNA samples) then biological replicates are more effective than technical replicates. Introducing technical replication requires a random effect in the ANOVA that is not handled (or handled incorrectly) by most of the software for microarray ANOVA. Because of this, I use LME when there are technical replicates, and this is slow, slow, slow. Also, if I want to "borrow strength" across genes, I have to write my own routines, which is a bit tougher to do, and to document for papers. At 12:48 AM 10/23/2003, Isaac Mehl wrote: >this design brings up a question i always have. is it "better" to do >all experiments in one channel (Cy5) and compare every sample to a >standard (cy3)? this way you can use less arrays or do more biological >replicates. IMHO getting repeated measurements of biological variation >is more important than dye swap. > >very interested to hear what people think about this topic since it is >integral to experimental design. > >-isaac > > > >DIF1,2 and 3 are different but similar drugs............... > >> > >> Slides 1-6 are treatment 1 (DIF1) Vs No treatment > >> Slide1 Cy5/Cy3 (DIF1/no treatment) > >> Slide2 Cy3/Cy5 (DIF1/no treatment) > >> Slide3 Cy5/Cy3 (DIF1/no treatment) > >> Slide4 Cy3/Cy5 (DIF1/no treatment) > >> Slide5 Cy3/Cy5 (DIF1/no treatment) > >> Slide6 Cy5/Cy3 (DIF1/no treatment) > >> > >> Slides 7-12 are treatment 2 (DIF2) Vs No treatment > >> Slide7 Cy5/Cy3 (DIF2/no treatment) > >> Slide8 Cy3/Cy5 (DIF2/no treatment) > >> Slide9 Cy5/Cy3 (DIF2/no treatment) > >> Slide10 Cy3/Cy5 (DIF2/no treatment) > >> Slide11 Cy3/Cy5 (DIF2/no treatment) > >> Slide12 Cy5/Cy3 (DIF2/no treatment) > >> > >> Slides 13-18 are treatment 3(DIF3) Vs No treatment > >> Slide13 Cy5/Cy3 (DIF3/no treatment) > >> Slide14 Cy3/Cy5 (DIF3/no treatment) > >> Slide15 Cy5/Cy3 (DIF3/no treatment) > >> Slide16 Cy3/Cy5 (DIF3/no treatment) > >> Slide17 Cy3/Cy5 (DIF3/no treatment) > >> Slide18 Cy5/Cy3 (DIF3/no treatment) > >> > >> I'd obviously like to compare across the different treatments DIF1,2 > >> and 3 >-- >-isaac mehl >gene expression lab (gele) >salk institute >10010 n. torrey pines rd. >la jolla ca. 92037 >http://genex.salk.edu > >_______________________________________________ >Bioconductor mailing list >Bioconductor@stat.math.ethz.ch >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Bioinformatics Consulting Center Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111