Hi, I am seeing a problem with the text output in my multi-channel experiment. I have different types of data so I want to normalize and score the channels separately. Therefore, I do not want to run summarizeReplicates. I think the following will show what I am doing: x <- readPlateList("Platelist.txt", name=experimentName, path=dataPath) x <- configure(x, "Description.txt", "Plateconf.txt", "Screenlog.txt", path=dataPath) xn <- normalizePlates(x, scale="multiplicative", method="median", varianceAdjust="none") xsc <- scoreReplicates(xn, sign="-", method="zscore") xsc at state[3]=TRUE getTopTable(cellHTSlist=list("raw"=x,"normalized"=xn, "scored"=xsc), file="testtable.txt") The output in testtable.txt (and similarly in writeReport's text output) has repeated values that are not what is actually in the cellHTS objects. raw'G01plate position well score wellAnno finalWellAnno raw_r1_ch1 raw_r2_ch1 raw_r1_ch2 raw_r2_ch2 median_ch1 diff_ch1 median_ch2 diff_ch2 raw/PlateMedian_r1_ch1 raw/PlateMedian_r2_ch1 raw/PlateMedian_r1_ch2 raw/PlateMedian_r2_ch2 normalized_r1_ch1 normalized_r2_ch1 normalized_r1_ch2 normalized_r2_ch2 3 145 G01 3.33 sample sample 80 80 80 80 80 0 80 0 0.0502 0.0502 0.0502 0.0502 0.05 0.05 0.05 0.05 1 7 A07 3.33 sample sample 80 80 80 80 80 0 80 0 0.051 0.051 0.051 0.051 0.051 0.051 0.051 0.051 3 202 I10 3.27 sample sample 110 110 110 110 110 0 110 0 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 Is it ok to force the scored state of xsc to TRUE? Please let me know if I'm using this correctly. I really appreciate your prior quick and helpful responses. thank you, Mark Dane Computational Biology Master Student Oregon Health and Science University > sessionInfo() R version 3.0.0 (2013-04-03) Platform: x86_64-apple-darwin10.8.0 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] grid parallel stats graphics grDevices utils datasets methods [9] base other attached packages: [1] lattice_0.20-15 gdata_2.12.0.2 cellHTS2_2.25.1 locfit_1.5-9.1 [5] hwriter_1.3 vsn_3.29.0 splots_1.27.0 genefilter_1.43.0 [9] Biobase_2.21.2 BiocGenerics_0.7.2 RColorBrewer_1.0-5 spade_1.9.0 [13] igraph0_0.5.7 loaded via a namespace (and not attached): [1] affy_1.39.2 affyio_1.29.0 annotate_1.39.0 [4] AnnotationDbi_1.23.11 BiocInstaller_1.11.1 Category_2.27.1 [7] DBI_0.2-7 feature_1.2.8 flowCore_1.27.15 [10] graph_1.39.0 GSEABase_1.23.0 gtools_2.7.1 [13] IRanges_1.19.8 ks_1.8.12 limma_3.17.12 [16] MASS_7.3-26 prada_1.37.0 preprocessCore_1.23.0 [19] RBGL_1.37.2 robustbase_0.9-7 rrcov_1.3-3 [22] RSQLite_0.11.3 splines_3.0.0 stats4_3.0.0 [25] survival_2.37-4 tools_3.0.0 XML_3.95-0.2 [28] xtable_1.7-1 zlibbioc_1.7.0

Dear Mark, getTopTable() has not been dealing properly with multi-channel data. Clearly it should, in order to be useful for cases such as yours. I have therefore now updated the function accordingly, and you can find it in cellHTS2 devel (2.25.3). You can see from the example below that it now outputs the replicate and channel information from the raw and summarized data correctly. Being able to have a separate score per channel is a feature that is potentially generally useful for users. Let me discuss this with the others and get back to you. I would generally advise against manually setting the state vector, but sometimes performing your own normalization or summarization is necessary so if you know what you are doing it is fine. # load example data library(cellHTS2) data(KcViabSmall) x <- KcViabSmall # simulate data for two channels ch1 <- matrix(runif(2*1152), 1152, 2, dimnames=list(1:1152,1:2)) ch2 <- matrix(runif(2*1152), 1152, 2, dimnames=list(1:1152,1:2)) assayData(x) <- do.call(assayDataNew,list(ch1=ch1,ch2=ch2)) # an example normalization xn <- normalizePlates(x, scale="multiplicative", method="median", varianceAdjust="none") xsc <- scoreReplicates(xn, sign="-", method="zscore") xsc <- summarizeReplicates(xsc, "mean") xsc <- summarizeChannels(xsc, fun=function(r1,r2) r1+r2) # getTopTable output tab <- getTopTable(cellHTSlist=list("raw"=x,"normalized"=xn, "scored"=xsc), file="testtable.txt") head(tab) Thanks again for your feedback! Please do keep us updated on any additional issues. Best wishes, Joe On May 23, 2013, at 7:14 AM, Mark Dane wrote: > Hi, > > I am seeing a problem with the text output in my multi-channel experiment. I have different types of data so I want to normalize and score the channels separately. Therefore, I do not want to run summarizeReplicates. I think the following will show what I am doing: > > x <- readPlateList("Platelist.txt", name=experimentName, path=dataPath) > x <- configure(x, "Description.txt", "Plateconf.txt", "Screenlog.txt", > path=dataPath) > xn <- normalizePlates(x, scale="multiplicative", method="median", > varianceAdjust="none") > xsc <- scoreReplicates(xn, sign="-", method="zscore") > xsc at state[3]=TRUE > getTopTable(cellHTSlist=list("raw"=x,"normalized"=xn, "scored"=xsc), > file="testtable.txt") > > The output in testtable.txt (and similarly in writeReport's text output) has repeated values that are not what is actually in the cellHTS objects. > > raw'G01plate position well score wellAnno finalWellAnno raw_r1_ch1 raw_r2_ch1 raw_r1_ch2 raw_r2_ch2 median_ch1 diff_ch1 median_ch2 diff_ch2 raw/PlateMedian_r1_ch1 raw/PlateMedian_r2_ch1 raw/PlateMedian_r1_ch2 raw/PlateMedian_r2_ch2 normalized_r1_ch1 normalized_r2_ch1 normalized_r1_ch2 normalized_r2_ch2 > 3 145 G01 3.33 sample sample 80 80 80 80 80 0 80 0 0.0502 0.0502 0.0502 0.0502 0.05 0.05 0.05 0.05 > 1 7 A07 3.33 sample sample 80 80 80 80 80 0 80 0 0.051 0.051 0.051 0.051 0.051 0.051 0.051 0.051 > 3 202 I10 3.27 sample sample 110 110 110 110 110 0 110 0 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 > > Is it ok to force the scored state of xsc to TRUE? Please let me know if I'm using this correctly. I really appreciate your prior quick and helpful responses. > > thank you, > > Mark Dane > Computational Biology Master Student > Oregon Health and Science University > > > >> sessionInfo() > R version 3.0.0 (2013-04-03) > Platform: x86_64-apple-darwin10.8.0 (64-bit) > > locale: > [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] grid parallel stats graphics grDevices utils datasets methods > [9] base > > other attached packages: > [1] lattice_0.20-15 gdata_2.12.0.2 cellHTS2_2.25.1 locfit_1.5-9.1 > [5] hwriter_1.3 vsn_3.29.0 splots_1.27.0 genefilter_1.43.0 > [9] Biobase_2.21.2 BiocGenerics_0.7.2 RColorBrewer_1.0-5 spade_1.9.0 > [13] igraph0_0.5.7 > > loaded via a namespace (and not attached): > [1] affy_1.39.2 affyio_1.29.0 annotate_1.39.0 > [4] AnnotationDbi_1.23.11 BiocInstaller_1.11.1 Category_2.27.1 > [7] DBI_0.2-7 feature_1.2.8 flowCore_1.27.15 > [10] graph_1.39.0 GSEABase_1.23.0 gtools_2.7.1 > [13] IRanges_1.19.8 ks_1.8.12 limma_3.17.12 > [16] MASS_7.3-26 prada_1.37.0 preprocessCore_1.23.0 > [19] RBGL_1.37.2 robustbase_0.9-7 rrcov_1.3-3 > [22] RSQLite_0.11.3 splines_3.0.0 stats4_3.0.0 > [25] survival_2.37-4 tools_3.0.0 XML_3.95-0.2 > [28] xtable_1.7-1 zlibbioc_1.7.0 > _______________________________________________ > 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