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Question: ChAMP Normalization on EPIC Methylation Data
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gravatar for yuabrahamliu
4 months ago by
yuabrahamliu0 wrote:

Hi there, 

I met a problem when using ChAMP to perform normalization on EPIC data. Actually, SWAN, PBC and BMIP can work well, but when I use FunctionalNormalization  to do that, I always got an Error. My code is, 

myNormfn <- champ.norm(beta=myLoad_B$beta,
                       rgSet=myLoad_B$rgSet,
                       mset=myLoad_B$mset,
                       resultsDir="./CHAMP_Placenta_Normalizationfn/",
                       method="FunctionalNormalization",  #BMIQ
                       plotBMIQ=TRUE,
                       arraytype="EPIC",
                       cores=50)

While the error message is,

[preprocessFunnorm] Background and dye bias correction with noob
Loading required package: IlluminaHumanMethylationEPICanno.ilm10b3.hg19
[dyeCorrection] Applying R/G ratio flip to fix dye bias
[preprocessFunnorm] Mapping to genome
[preprocessFunnorm] Quantile extraction
[preprocessFunnorm] Normalization
Error in getBeta(preprocessFunnorm(rgSet))[rownames(beta), ] :
  subscript out of bounds
Calls: champ.norm
Execution halted

I don't know why there is always such a problem. Is there anything to do with the required package IlluminaHumanMethylationEPICanno.ilm10b3.hg19, because when I install ChAMP, actually what will also be installed automatically is IlluminaHumanMethylationEPICanno.ilm10b2.hg19, not IlluminaHumanMethylationEPICanno.ilm10b3.hg19. It is 10b2, not 10b3, and I need to install 10b3 by specially when did normalization. I don't know whether this is the point, or there are any other causes. Could anyone give me some help? Thank you so much!

My R version is 3.5.0

Best wishes,

Yu

ADD COMMENTlink written 4 months ago by yuabrahamliu0

At the moment, functional normalization does not greatly enhance results over the underlying ssNoob performance on EPIC arrays (cf. https://academic.oup.com/bioinformatics/article/33/4/558/2666344 ). You might consider using straight noob, as funnorm's results will be dataset dependent, and ssNoob is a single-sample approach. The choice depends on your experiment.

 

Alternatively, the best results we have seen on TARGET, TCGA, and other varied datasets have come from sesame ( https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gky691/5061974 ) which unfortunately is not yet integrated into a ChAMP workflow (although sesame::sesamize() could easily allow the authors of ChAMP to do this).  SeSAMe will be part of the imminent BioC-3.8 release (http://bioconductor.org/packages/devel/bioc/html/sesame.html ) and if we knew of any better approach to preprocess Illumina methylation data, we'd just use that instead, so we'd love to see its strengths integrated widely.

ADD REPLYlink written 4 months ago by Tim Triche4.2k

OK. Thank you so much.

ADD REPLYlink written 4 months ago by yuabrahamliu0
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