Hi James, BioCers, I wanted to follow up your suggestions for dealing with technical variation across 2 hybs by giving you all available information for your consideration. Aim: To characterise transcriptomes of three different, neighbouring neuron populations within mouse brain, relative to each other. Methods: To do this, the 3 populations were captured from four biological replicates. Thus 12 arrays. Two complete replicates were hybed on day 1- thus Hyb A, and the second two rep.s a month later- Hyb B. *Hyb A used old microarrays ~18 months past expiration. Hyb B used unexpired arrays. All other (known) factors are equal. Results: Hyb A shows significantly lower intensities than Hyb B. See summary of "#Unnormalised data:" below. QPCR shows good concordance between Hyb A and B ~8/10 genes analsyed show the same relative changes. Discussion: I was thinking (with Douglas Altman in hand, but no statistician to interrogate!): An ANOVA on the paired data of the 3 tissues per animal. Because my 3 tissues come from the same animal, can i increase statistical power by employing a paired test per animal per hyb. I could normalise all samples from Hyb A. and analyse them with a duplicate, paired sample ANOVA approach to identify differential expression bwtn the 3 tissues. This could be repeated for Hyb B. My Qs: -a valid approach? -how to best combine Hyb A and B. to gain a greater statistical power? -exactly what test , ANOVA variation is employed? -R implementation (or any other?)? -controlling for false discovery? 2. Employ a non-parametric version of the above? 3. RMA all of it, hope for the best, and then employ ANOVA or some other test? RMA normalized data is not so bad (see summary "#RMA normalized data:" below), and maybe not so good... Any and all guidance very much appreciated. thanks in advance, Karl #Unnormalized data: > dat <- ReadAffy() > dat.rma <- rma(dat, normalize=FALSE) Background correcting Calculating Expression > apply(exprs(dat.rma),2,summary) Tco1A.CEL Tco2A.CEL Tco3B.CEL Tco4B.CEL Tmi1A.CEL Tmi2A.CEL Min. 1.633 1.713 1.869 2.431 2.027 1.736 1st Qu. 2.627 2.751 3.234 3.554 2.831 2.882 Median 3.329 3.320 4.933 4.952 3.433 3.588 Mean 4.153 3.902 5.572 5.741 4.330 4.261 3rd Qu. 5.147 4.541 7.534 7.556 5.254 5.151 Max. 14.180 13.640 14.370 14.280 14.240 13.880 Tmi3B.CEL Tmi4B.CEL Tsh1A.CEL Tsh2A.CEL Tsh3B.CEL Tsh4B.CEL Min. 1.771 2.575 1.607 1.902 1.771 2.360 1st Qu. 3.280 3.716 2.541 2.850 3.197 3.940 Median 4.986 4.978 3.183 3.357 4.833 5.488 Mean 5.629 5.762 3.983 4.032 5.493 6.092 3rd Qu. 7.606 7.449 4.882 4.629 7.426 7.892 Max. 14.300 14.180 14.070 13.990 14.230 14.340 #RMA normalized data: > eset <- justRMA(filenames=list.celfiles()) Background correcting Normalizing Calculating Expression > apply(exprs(eset),2,summary) Tco1A.CEL Tco2A.CEL Tco3B.CEL Tco4B.CEL Tmi1A.CEL Tmi2A.CEL Min. 1.945 2.077 2.014 2.070 1.970 2.050 1st Qu. 3.394 3.574 3.114 3.135 3.381 3.483 Median 4.469 4.533 4.552 4.435 4.474 4.472 Mean 5.191 5.110 5.221 5.192 5.199 5.133 3rd Qu. 6.539 6.114 6.947 6.866 6.589 6.260 Max. 14.180 14.170 14.120 14.170 14.170 14.180 Tmi3B.CEL Tmi4B.CEL Tsh1A.CEL Tsh2A.CEL Tsh3B.CEL Tsh4B.CEL Min. 1.930 2.056 2.015 2.028 1.815 1.919 1st Qu. 3.106 3.190 3.436 3.524 3.132 3.134 Median 4.520 4.426 4.497 4.484 4.548 4.450 Mean 5.219 5.194 5.203 5.140 5.224 5.198 3rd Qu. 6.949 6.824 6.537 6.241 6.938 6.887 Max. 14.130 14.070 14.180 14.130 14.110 14.150 > sessionInfo() Version 2.3.0 (2006-04-24) i386-pc-mingw32 attached base packages: [1] "tools" "methods" "stats" "graphics" "grDevices" "utils" "datasets" "base" other attached packages: affyPLM gcrma matchprobes affydata mouse4302cdf vsn limma affy affyio Biobase "1.8.0" "2.4.1" "1.4.0" "1.8.0" "1.12.0" "1.10.0" "2.7.3" "1.10.0" "1.0.0" "1.10.0" -- Karl Brand <k.brand at="" erasmusmc.nl=""> Department of Cell Biology and Genetics Erasmus MC Dr Molewaterplein 50 3015 GE Rotterdam lab +31 (0)10 408 7409 fax +31 (0)10 408 9468