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Hi ,
We have a set of 200 samples that we have analysed with EPIC array. I have load the idat.files using champ.load, with autoimpute=T. The output has no missing values, but when I normalize using champ.norm to run champ.SVD I get missing value again. is that a normal behaviour? should I impute again the matrix after using champ.norm?
Thanks
here is my code:
myLoad_noXY <- champ.load(directory=getwd(),
filterXY = TRUE,
SampleCutoff = 0.9,
filterDetP=F,
filterBeads=F,
ProbeCutoff = 0.02,
detPcut = 0.01,
beadCutoff = 0.05,
filterMultiHit = F,
filterSNPs = F,
filterNoCG = F,
arraytype="EPIC",
autoimpute = TRUE,
method = "minfi")
# Normalise data
myNorm <- champ.norm(beta=myLoad_noXY$beta,
rgSet=myLoad_noXY$rgSet,
mset=myLoad_noXY$mset,
resultsDir="./CHAMP_Normalization_noXY_imputed/",
method="BMIQ",
plotBMIQ=FALSE,
arraytype="EPIC",
cores=3)
# SVD
champ.SVD(beta = myNorm,
rgSet=NULL,
pd=myLoad_noXY$pd,
RGEffect=FALSE,
PDFplot=TRUE,
Rplot=FALSE,
resultsDir="./CHAMP_SVDimages_noXY/")
Tnanks @Yuan Tian, I have pasted my code in the question
Hello:
I just checked the code. I wonder if you have checked after loading, is there missing value in your myLoad$beta variable? In your code, I can see filterDetP=F. I think it means imputation would not be done because NA values are actually induced by detect p value, if filterDetP=F, it could mean imputation is not working.
By the way, when you run champ.load() code, have you see the message like imputation on your screen? BMIQ method used two quantile function projection solution to do normalization, I think if original beta matrix is free of NA or negative value, the normalized result should not induce NA.
Anyway, could you tell me how many NAs in beta matrixes: myLoad$beta, myNorm?
Best
Yuan Tian