Hi all,

I use minfi for pre-processing of EPIC methylation data, so from my last step I have an RGSet. Now I've read that it is good to combine noob with BMIQ or preprocessQuantile for normalization.

However, I wonder how this can be done in R with the different output objects. Assume I do noob first (noob= preprocessNoob(RGSet), the output is a GenomicRatioSet I guess.

How can I then do preprocess Quantile (needs RGSet) or BMIQ (needs beta matrix?) afterwards?

Thanks for help.

Quantile normalization doesn't need an RGSet. Why do you think that? From ?preprocessQuantile:

Usage:

     preprocessQuantile(object, fixOutliers = TRUE, removeBadSamples = FALSE,
                        badSampleCutoff = 10.5, quantileNormalize = TRUE,
                        stratified = TRUE, mergeManifest = FALSE, sex = NULL,
                        verbose = TRUE)

Arguments:

  object: An object of class 'RGChannelSet' or '[Genomic]MethylSet'.

And preprocessFunnorm by default first calls preprocessNoob, and then does an arguably better/smarter quantile normalization using the first N PCs of the background probes. So you could just use that. In addition, ?BMIQ (from wateRmelon) says

Usage:

     BMIQ(beta.v, design.v, nL = 3, doH = TRUE, nfit = 50000, th1.v = c(0.2, 0.75), th2.v = NULL, niter = 5, tol = 0.001, plots = TRUE, sampleID = 1, pri=TRUE)
     ## S4 method for signature 'MethyLumiSet'
     BMIQ(beta.v, nL=3, doH=TRUE, nfit=5000, th1.v=c(0.2,0.75), th2.v=NULL, niter=5, tol=0.001, plots=FALSE,  pri=FALSE ) 
     CheckBMIQ(beta.v, design.v, pnbeta.v)

Arguments:

  beta.v: vector consisting of beta-values for a given sample, or a
          MethyLumiSet or MethylSet containing multiple samples. For
          the MethyLumiSet and MethylSet methods, this is the only
          required argument, and the function will be run on each
          sample.

So you don't need a beta matrix for BMIQ either.