Dear Gordon, Thank you so much for your comments. One more question about the first question asked in my previous post where I asked about how to supply the correct factor in the normalization step. I would like use the total read count normalization method to normalize the data then use the edgeR to test my multi-factor models as in my previous post. The total read count normalization is given as X_ij/(N_j/mean(N))=X_ij*mean(N)/N_j, where X_ij is the read count of gene i sample j, N_j is the library size of sample j, and mean(N) is the mean of library sizes over all samples. My question is what is the input for y$samples$norm.factors? Can I do as the following: y$samples$norm.factors = N/mean(N)? Where N is the vector of library size of all samples, and mean(N) is the mean of library sizes over all sample. Or could you please give me some suggestion? Thank you! Yanzhu --------------------------------------------------- Date: Fri, 7 Feb 2014 07:25:17 -0800 (PST) > From: "Yanzhu [guest]" <guest at="" bioconductor.org=""> > To: bioconductor at r-project.org, mlinyzh at gmail.com > Subject: [BioC] EdgeR multi-factor testing questions > > > Dear Gordon, > > Thank you so much for your comments. I have updated my code and get the > different results for TMM and Upper quartile normalization methods. > > I have two more question regarding the normalization issue. I have tried > different normalization methods and would like to compare their > performance. My questions are: > > 1. In the users' guide 2.5.6, it mentions that normalization takes the > form of correction factors that enter into the statistical model. Such > correction factors are usually computed internally by edgeR functions, > but it is also possible for a user to supply them.I would like to supply > the correct factor to edgeR, how could I do this? Just enter in your own values: y$samples$norm.factors <- yourvalues > 2. I also would like to compare the testing results of normalized data > with the results of raw data (without normalizing the data)? Could I > just skip the the normalization step as below? Yes. Gordon > group<-paste(L,S,R,sep=".") > design<-model.matrix(~L+R+S+L:R+L:S+R:S+L:R:S) > y<-DGEList(counts=counts,group=group) > #y<-calcNormFactors(y,method="upperquartile",p=0.75) ##skip this step > > y<-estimateGLMCommonDisp(y,design) > y<-estimateGLMTagwiseDisp(y,design) > > fiteUQ_LRS<-glmFit(y,design,offset=offset ) > > Thanks. > > > Yanzhu > > -- output of sessionInfo(): > 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] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq_1.12.1 lattice_0.20-15 locfit_1.5-9.1 Biobase_2.20.1 BiocGenerics_0.6.0 edgeR_3.2.4 limma_3.16.8 loaded via a namespace (and not attached): [1] annotate_1.38.0 AnnotationDbi_1.22.6 DBI_0.2-7 genefilter_1.42.0 geneplotter_1.38.0 grid_3.0.1 IRanges_1.18.4 [8] RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.0.1 stats4_3.0.1 survival_2.37-4 XML_3.98-1.1 xtable_1.7-1 > -- Sent via the guest posting facility at bioconductor.org.