I am trying to use NanoStringDiff for differential expression analysis of a nanostring data-set with 506 endogenous genes in the set. I was wondering how/if there is a way to output the normalized data that NanoStringDiff uses to run the differential expression LRT tests? I have been able to run the differential expression analyses correctly (I hope!), but now would like to know if there is a way to access the normalized data to use for PCA plots, heatmaps, etc? I tried assay(exprs) but it just gave me the raw counts. Thanks!

#Load data

path<-paste(dir,"nanostring_R.csv",sep="/")
designs <- data.frame(group=c("WT_IL13", "WT_IL13", "WT_IL13", "WT_CTRL", "WT_CTRL", "WT_CTRL", "RA1_IL13", "RA1_IL13", "RA1_IL13", "RA1_CTRL", "RA1_CTRL", "RA1_CTRL"))

#Create a Nanostring dataset
nanostringdata <- createNanoStringSetFromCsv(path = path, header = TRUE, designs = designs)

#Run DE analysis
pheno=pData(nanostringdata)
group=pheno$group
design.full=model.matrix(~0+group)
design.full

NanoStringData_Norm <- estNormalizationFactors(nanostringdata)

#Get Results for pairwise contrasts
result_WT <- glm.LRT(NanoStringData_Norm,design.full,contrast=c(0,0,-1,1))

I don't think there is a direct accessor, but this is what is done to the data prior to fitting any model:

    c = positiveFactor(NanoStringData)
    d = housekeepingFactor(NanoStringData)
    k = c * d
    lamda_i = negativeFactor(NanoStringData)
    Y = exprs(NanoStringData)
    Y_n = sweep(Y, 2, lamda_i, FUN = "-")
    Y_nph = sweep(Y_n, 2, k, FUN = "/")
    Y_nph[Y_nph <= 0] = 0.1

And then 

     Y_nph <- log(Y_nph)

will give you data that you can plot.