Hi I have two sets of data generated from Affymetrix mogene 1.0 ST and the Affmetrix mogene 1.1 ST Array. I am trying to normalise them together with the following but it did not work. How do you read two types of CEL files with identical gene lists but different chip dimensions? >library(oligo) >library(pd.mogene.1.0.st.v1) >library(pd.mogene.1.1.st.v1) >celFiles <- list.celfiles() >Rawdata <- read.celfiles(celFiles) All the CEL files must be of the same type. Error: checkChipTypes(filenames, verbose, "affymetrix", TRUE) is not TRUE Cheers Joel Z Ma, PhD Dept. of Microbiology and Immunology The Peter Doherty Institute for Infection and Immunity University of Melbourne 792 Elizabeth Street Parkville Victoria, 3000 Ph: +61 3 83440775 E-mail: jzma@unimelb.edu.au [[alternative HTML version deleted]]

I am trying to use CellMix to generate a signature(basis) matrix for whole blood deconvolution based on some inhouse data. I have gene expression data from 16 immune cell types. I had a few questions for you. I would really appreciate your response. 1. I am trying to use Abass method (markerScoreAbbas) for identifying the markerset. When I try to do that based on p.value as statitic, it works fine. But when I try to do the same with t.statistic as the statitic it gives me an error. Following is the r code I am using along with the error message. I have also included the session info along with it. 2. In terms of interpretating the results from the call "markerScore Abbas" I have the following column headers:top,p.value,p.value2,dm2,statistic 2,dmM2,fold2,mMfold2,top2,p.value3,dm3,statistic3,dmM3,fold3,mMfold3,t op3,p.valuea. Could you please explain what's the difference between top, top2 and top3?Is top indices for one vs rest; top2 for one vs second highest and top3 for one vs. third highest?b. In that case why isn't there a column called fold which would be the fold change that goes with top?c. Are the p-values fdr corrected? Also I got exact same p-values for p.value and p.value3 3. How do I know as to which index in the top columns match to which celltype in f? 4. I finally want to optimize my signature matrix to come up with the minimum number of genes without losing accuracy. Could you please suggest a way of doing that? I don't have cell counts associated with the RNA extracts for the individual samples. I would really appreciate your response and suggestions.Best regards,Sam.> setwd("C:/Users/bandyops/Desktop/WB_Decon_2014/cell_mix")> library("CellMix")Loading required package: pkgmakerLoading required package: registryLoading required package: NMFLoading required package: rngtoolsLoading required package: clusterNMF - BioConductor layer [OK] | Shared memory capabilities [NO: windows] | Cores 7/8Loading required package: csSAMLoading required package: compilerLoading required package: stringrLoading required package: GSEABaseLoading required package: annotateLoading required package: AnnotationDbiLoading required package: graphWarning messages:1: package ‘pkgmaker’ was built under R version 3.0.3 2: package ‘NMF’ was built under R version 3.0.3 3: package ‘rngtools’ was built under R version 3.0.3 4: package ‘cluster’ was built under R version 3.0.3 > imm <- read.table("scaled_rma_normalized_qced.txt", header=T, row.names=1, sep="\t")> imm <- as.matrix(imm)> target <- read.table("target.txt", header=T, row.names=1, sep="\t")> > if (sum(sort(rownames(target)) == sort(colnames(imm))) != length(rownames(target))) {+ stop ("ERROR: target rownames and RMA colnames do not match")+ }> > f <- factor(target$Cells)> scoring_pvalue <- markerScoreAbbas(imm, f, statistic = "p.value", ntop = 3, log = F, lbase=3)> scoring_tstat <- markerScoreAbbas(imm, f, statistic = "statistic", ntop = 3, log = F, lbase=3)Error in res[, statistic] : subscript out of bounds> traceback()2: cbind(res[, 1L, drop = FALSE], res[, statistic], res[, -1L, drop = FALSE])1: markerScoreAbbas(imm, f, statistic = "statistic", ntop = 3, log = F, lbase = 3) > sessionInfo()R version 3.0.1 (2013-05-16)Platform: x86_64-w64-mingw32/x64 (64-bit)locale:[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252[4] LC_NUMERIC=C LC_TIME=English_United States.1252 attached base packages:[1] compiler parallel stats graphics grDevices utils datasets methods base other attached packages: [1] CellMix_1.6 GSEABase_1.22.0 graph_1.38.3 annotate_1.38.0 AnnotationDbi_1.22.6 [6] stringr_0.6.2 csSAM_1.2.4 NMF_0.20.5 Biobase_2.20.1 BiocGenerics_0.6.0 [11] cluster_1.15.2 rngtools_1.2.4 pkgmaker_0.20 registry_0.2 loaded via a namespace (and not attached): [1] beeswarm_0.1.6 bibtex_0.3-6 BiocInstaller_1.10.4 codetools_0.2-8 colorspace_1.2-4 [6] corpcor_1.6.6 DBI_0.2-7 dichromat_2.0-0 digest_0.6.4 doParallel_1.0.8 [11] foreach_1.4.2 genefilter_1.42.0 ggplot2_0.9.3.1 grid_3.0.1 gridBase_0.4-7 [16] gtable_0.1.2 gtools_3.4.0 IRanges_1.18.4 iterators_1.0.7 labeling_0.2 [21] limSolve_1.5.5 lpSolve_5.6.7 MASS_7.3-31 matrixStats_0.8.14 munsell_0.4.2 [26] plyr_1.8.1 preprocessCore_1.22.0 proto_0.3-10 quadprog_1.5-5 R.methodsS3_1.6.1 [31] RColorBrewer_1.0-5 Rcpp_0.11.1 reshape2_1.2.2 RSQLite_0.11.4 scales_0.2.3 [36] splines_3.0.1 stats4_3.0.1 survival_2.37-7 tools_3.0.1 XML_3.98-1.1 [41] xtable_1.7-3 [[alternative HTML version deleted]]

Hi Joel, The short answer is that you can't. You can only process the two sets of arrays separately. Best, Jim On 5/17/2014 9:08 AM, Joel Ma wrote: > Hi > > I have two sets of data generated from Affymetrix mogene 1.0 ST and the Affmetrix mogene 1.1 ST Array. I am trying to normalise them together with the following but it did not work. How do you read two types of CEL files with identical gene lists but different chip dimensions? > >> library(oligo) >> library(pd.mogene.1.0.st.v1) >> library(pd.mogene.1.1.st.v1) >> celFiles <- list.celfiles() >> Rawdata <- read.celfiles(celFiles) > All the CEL files must be of the same type. > Error: checkChipTypes(filenames, verbose, "affymetrix", TRUE) is not TRUE > > > Cheers > > Joel Z Ma, PhD > > Dept. of Microbiology and Immunology > The Peter Doherty Institute for Infection and Immunity > University of Melbourne > 792 Elizabeth Street > Parkville > Victoria, 3000 > > Ph: +61 3 83440775 > E-mail: jzma at unimelb.edu.au > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > 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

Hi Joel, You might try the SCAN() function in the SCAN.UPC package for this. You can pass it a wildcard specifying the location of the CEL files, and it will normalize them separately and then merge them based on overlapping features. I haven?t tried the exact thing you?re describing, but it should work. Let me know if it doesn?t. Regards, -Steve On 5/18/14, 4:00 AM, "bioconductor-request at r-project.org" <bioconductor-request at="" r-project.org=""> wrote: >Date: Sat, 17 May 2014 13:08:54 +0000 >From: Joel Ma <jzma at="" unimelb.edu.au=""> >To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> >Subject: [BioC] Normalising both Affy mogene 1.0 and 1.1 together >Message-ID: > <ab356226b32de84f818cba2cfc4bee335f30d889 at="" 000s-ex-mbx-="" qs3.unimelb.edu.au=""> > >Content-Type: text/plain > >Hi > >I have two sets of data generated from Affymetrix mogene 1.0 ST and the >Affmetrix mogene 1.1 ST Array. I am trying to normalise them together >with the following but it did not work. How do you read two types of CEL >files with identical gene lists but different chip dimensions? > >>library(oligo) >>library(pd.mogene.1.0.st.v1) >>library(pd.mogene.1.1.st.v1) >>celFiles <- list.celfiles() >>Rawdata <- read.celfiles(celFiles) > >All the CEL files must be of the same type. >Error: checkChipTypes(filenames, verbose, "affymetrix", TRUE) is not TRUE > > >Cheers > >Joel Z Ma, PhD > >Dept. of Microbiology and Immunology >The Peter Doherty Institute for Infection and Immunity >University of Melbourne >792 Elizabeth Street >Parkville >Victoria, 3000 > >Ph: +61 3 83440775 >E-mail: jzma at unimelb.edu.au >