I should note that ideally you would first background correct and normalize and then do what I showed below: Dilution <- bg.correct(Dilution, "rma") Dilution <- normalize(Dilution) Best, Jim On 2/20/2013 11:07 AM, James W. MacDonald wrote: > Hi Hugh, > > It's simple enough to get these data on a probeset-by-probeset basis: > > > library(affydata) > > data(Dilution) > > medpolish(log2(pm(Dilution, "1007_s_at"))) > 1: 5.536431 > 2: 4.554366 > Final: 4.528792 > > Median Polish Results (Dataset: "log2(pm(Dilution, "1007_s_at"))") > > Overall: 8.618676 > > Row Effects: > 1007_s_at1 1007_s_at2 1007_s_at3 1007_s_at4 1007_s_at5 1007_s_at6 > -0.59973136 0.29592898 0.78257437 2.05732074 2.39466709 1.73948500 > 1007_s_at7 1007_s_at8 1007_s_at9 1007_s_at10 1007_s_at11 1007_s_at12 > -0.04027435 -0.06544218 -0.67467511 0.69974913 0.51478816 -0.13604974 > 1007_s_at13 1007_s_at14 1007_s_at15 1007_s_at16 > -0.15326752 -1.90470573 -1.11026444 0.04027435 > > Column Effects: > 20A 20B 10A 10B > 0.5728390 -0.1144511 0.1202443 -0.6362839 > > Residuals: > 20A 20B 10A 10B > 1007_s_at1 -0.18749394 -0.0465129 0.0407198 0.17422802 > 1007_s_at2 0.13826450 0.0295685 -0.1240571 -0.03039400 > 1007_s_at3 0.07439714 -0.0317711 0.0259779 -0.03874874 > 1007_s_at4 -0.03633972 0.1518405 -0.0181643 0.01217539 > 1007_s_at5 0.07528392 -0.0683772 0.0625840 -0.09050186 > 1007_s_at6 0.00379623 0.0646288 -0.0087639 -0.11399244 > 1007_s_at7 0.04055849 -0.0460984 -0.0440103 0.03802140 > 1007_s_at8 -0.11484593 0.0062318 0.0840782 -0.00705727 > 1007_s_at9 -0.02900022 -0.0267107 0.1319877 0.02588521 > 1007_s_at10 -0.12805213 0.1904883 0.0268966 -0.03288549 > 1007_s_at11 -0.00067114 0.0083397 -0.1167177 0.00067114 > 1007_s_at12 0.02934276 -0.0062318 -0.2409272 0.00540625 > 1007_s_at13 0.00067114 0.0109860 -0.1899048 -0.00067114 > 1007_s_at14 -0.09698507 -0.0145570 0.0087639 0.07814337 > 1007_s_at15 -0.26554742 -0.0828937 0.1806296 0.08206822 > 1007_s_at16 0.03967331 0.1902100 -0.2177162 -0.03967331 > > And doing it on a whole array isn't that expensive: > > > pms <- pm(Dilution, LISTRUE = TRUE) > > system.time(meds <- lapply(pms, medpolish, trace.iter=FALSE)) > user system elapsed > 62.387 0.033 62.439 > > Best, > > Jim > > > > On 2/20/2013 7:39 AM, Hugh Shanahan wrote: >> Hi, >> we would like to compute the parameters (i.e, the other >> parameters that are computed via the median polish algorithm) that >> are computed in the final summarisation step in RMA. In particular, >> we'd like to determine the parameter alpha_i that tells us how the >> estimate for the expression varies as a function of the probe. I >> guess it's possible to open up the relevant C-code and find it there, >> but are there are libraries that allow one to do it more easily ? I >> saw that affyPLM appears to be doing something similar - is this >> where we should be looking ? >> >> Many thanks, >> Hugh >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099