I think I over-thought this problem. You probably wouldn't have to do much work to get the values you want. abatch <- ReadAffy() abatch <- bg.correct.mas(abatch) pmcorrect.mas.ims(pm(abatch, "someprobeID"), mm(abatch, "someprobeID")) should do it. pmcorrect.mas.ims <- function (pms, mms, contrast.tau = 0.03, scale.tau = 10, delta = 2^(-20)){ diff <- log2(pms) - log2(mms) delta <- rep(delta, nrow(diff)) out <- matrix(NA, ncol = dim(diff)[2], nrow = dim(diff)[1]) for (i in 1:ncol(diff)) { sb <- tukey.biweight(diff[, i]) pps.pm <- pms[, i] pps.mm <- mms[, i] pps.im <- pps.mm j <- pps.mm >= pps.pm) & (sb > contrast.tau) pps.im[j] <- pps.pm[j]/2^sb j <- pps.mm >= pps.pm) & (sb <= contrast.tau) pps.im[j] <- pps.pm[j]/2^(contrast.tau/(1 + (contrast.tau - sb)/scale.tau)) out[,i] <- pps.im } out } > library(affy) > data(affybatch.example) > affybatch.example <- bg.correct.mas(affybatch.example) > pmcorrect.mas.ims(pm(affybatch.example, "A28102_at"), mm(affybatch.example, "A28102_at")) [,1] [,2] [,3] [1,] 146.5810 116.02368 122.06893 [2,] 141.1703 122.70979 114.68573 [3,] 129.8570 109.14092 103.36482 [4,] 127.8895 96.35865 113.20909 [5,] 113.9200 112.38565 108.77917 [6,] 128.8732 140.60497 120.59229 [7,] 129.6603 121.92319 120.10008 [8,] 137.7271 126.83944 105.62900 [9,] 120.0194 169.11926 101.88818 [10,] 131.1359 175.01877 105.62900 [11,] 133.7921 143.35807 97.26137 [12,] 121.0031 135.68871 99.42711 [13,] 130.3489 128.80595 108.58228 [14,] 127.8895 111.89402 104.84146 [15,] 122.9707 117.00693 102.18351 [16,] 131.3327 161.25325 108.28695 > pm(affybatch.example, "A28102_at") 20A 20B 10A A28102_at1 149.0 118.0 124.0 A28102_at2 143.5 124.8 116.5 A28102_at3 132.0 111.0 105.0 A28102_at4 130.0 98.0 115.0 A28102_at5 115.8 114.3 110.5 A28102_at6 131.0 143.0 122.5 A28102_at7 131.8 124.0 122.0 A28102_at8 140.0 129.0 107.3 A28102_at9 122.0 172.0 103.5 A28102_at10 133.3 178.0 107.3 A28102_at11 136.0 145.8 98.8 A28102_at12 123.0 138.0 101.0 A28102_at13 132.5 131.0 110.3 A28102_at14 130.0 113.8 106.5 A28102_at15 125.0 119.0 103.8 A28102_at16 133.5 164.0 110.0 > mm(affybatch.example, "A28102_at") 20A 20B 10A A28102_at1 847.0 694.0 999.0 A28102_at2 860.3 667.3 1084.8 A28102_at3 815.3 650.0 1057.0 A28102_at4 855.0 664.0 1038.0 A28102_at5 729.0 594.5 1096.0 A28102_at6 711.0 619.3 933.0 A28102_at7 798.0 642.0 803.0 A28102_at8 800.0 631.5 734.0 A28102_at9 862.0 663.3 892.0 A28102_at10 835.5 605.3 1070.0 A28102_at11 886.0 614.0 1040.3 A28102_at12 900.0 589.5 981.0 A28102_at13 941.8 629.3 1062.0 A28102_at14 899.0 608.3 1061.5 A28102_at15 846.0 594.8 961.0 A28102_at16 860.0 538.0 927.0 Best, Jim James W. MacDonald wrote: > Wang, Yonghong (NIH/NCI) [C] wrote: > >>Hi, All: >>As part of the suggestions from a reviewer of my submitted paper, I am >>asked to provide the IM (Ideal Mismatch) values generated from MAS5 for >>some probes of some probeset. I know BioConductor can provide both PM >>and MM intensities for each individual probes, would someone please tell >>me whether I can also get IM values (when applicable) from cel file by >>using BioConductor? If we can, how? > > > The short answer is no. > > The long answer is yes, but it would take some work to do so. The work > is done by the function pmcorrect.mas(), which is called on each > probeset individually. You would have to background correct your > AffyBatch using bgcorrect.mas(), then for whichever probesets you are > interested in, you could either create your own function based on > pmcorrect.mas() that returns the IM values, and just run that function > on the probeset in question. Or you could just debug() the existing > pmcorrect.mas(), and when the IM values you are interested in appear, > you could dump them into the .GlobalEnv using the <<- operator. > > Note that pmcorrect.mas() takes a ProbeSet object as input. What is done > in expresso() is to make an empty ProbeSet object, then get the PM and > MM values for a particular probeset (using a combination of get() and > intensity(), although you could probably just use pm(abatch, > "probesetID")) and then stick these values in the pm and mm slots of the > ProbeSet. This is done in computeExprSet(). > > If it sounds like a lot of work, it is. If you really need these data, > you should look at the functions pmcorrect.mas(), and computeExprSet() > to see how it is done and to figure out how to do it yourself. > > To see the functions you can either download the sources from BioC and > grep them out of the R directory (preferable), or you can just load the > affy package and type > > pmcorrect.mas > > and > > showMethods(computeExprSet, class = "AffyBatch", includeDefs = TRUE) > > Best, > > Jim > > > >>Thanks a lot for your help. >> >>Best regards >> >>YH Wang >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at stat.math.ethz.ch >>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 Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. 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