Here are some alternative codes for the data transformation and normalization process. We have compared its performance with log2-quantile, but not rank invariant.Note that VST transformation, unlike log2, can take negative values. However, our experience also suggest that background correction is optional.-Simon> library(lumi) # load the library > # specify the file name output from Bead Studio > fileName <- 'Barnes_gene_profile.txt' > # Read the data and create a LumiBatch object > example.lumi <- lumiR(fileName) > # Transfer Illumina data as nuID annotated > example.lumi <- addNuId2lumi(example.lumi, lib=Human.lumi) > # Quality control based on the raw data > lumi.Q <- lumiQ(example.lumi) # (optional) > # As an example, plot the sample relations of QC > plot(lumi.Q, type='sampleRelation') > # Do default VST variance stabilizing transform > lumi.T <- lumiT(example.lumi) > # Do RSN between microarray normaliazation > lumi.N <- lumiN(lumi.T) > # Quality control after normalization > lumi.N.Q <- lumiQ(lumi.N) # (optional) > # Extract expression data for further processing > dataMatrix <- exprs(lumi.N) > # LIMMA modeling, then Nieves Velez de Mendizabal wrote: > We are analyzing some data of Illumina. There are three kind of > normalization. First of them is the method of rank invariant > normalization, recommended by Illumina, and we would like to apply it: > > > BSData.bgnorm = backgroundNormalise(BSData) > T = apply(exprs(BSData.bgnorm), 1, mean) > BSData.rankinv = assayDataElementReplace(BSData.bgnorm, "exprs", > rankInvariantNormalise(exprs(BSData.bgnorm), T)) > > > But in BSData.rankinv I have negative values so I cannot apply the > method lmFit in order to analyze the differential expression because of > the log2 transformation applied. > > fit = lmFit(log2(exprs(BSData.rankinv)), design) > > Are these two methods (rank inv method and lmFit) incompatible? > What kind of normalization should I use in order to search > differentially expressed genes in micro arrays of Illumina? > > Thanks