Question: missing value in beta matrix after normalization wich champ.norm
0
gravatar for jfertaj
21 months ago by
jfertaj20
United Kingdom
jfertaj20 wrote:

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/")
champ champs.svd() champ.norm • 295 views
ADD COMMENTlink modified 21 months ago • written 21 months ago by jfertaj20
Answer: missing value in beta matrix after normalization wich champ.norm
0
gravatar for Yuan Tian
21 months ago by
Yuan Tian120
University College London
Yuan Tian120 wrote:

Hello:

I don't think normalization step would induce missing value. Actually, missing value is not allowed to exist for data to be normalized. Could you tell me more about your code? 

Best

Yuan Tian

ADD COMMENTlink written 21 months ago by Yuan Tian120

Tnanks @Yuan Tian, I have pasted my code in the question

ADD REPLYlink written 21 months ago by jfertaj20

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

ADD REPLYlink written 21 months ago by Yuan Tian120
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