Dear All,
I would like to normalise data from Expo comprising of over 1000 CEL files.
The first approach I have implemented resulted in most of the genes being
positively correlated with each other. What could be the problem? Here is my
code. There is no error in the whole analysis its only the results are not
consistent with what i know from other smaller datasets which I have
normalised using bioconductor packages.
verbose <- Arguments$getVerbose(-8); timestampOn(verbose)
datasetname <- "expo"
chip1<- "HGU133Plus2_Hs_ENSG"
expo_cel_set <- AffymetrixCelSet$fromName(datasetname,
chipType=chip1,checkChipType=FALSE)
expo_full_set_bc <- RmaBackgroundCorrection(expo_cel_set)
expo_full_set_bc_processed <- process(expo_full_set_bc)
qn1 <- QuantileNormalization(expo_full_set_bc_processed, typesToUpdate="pm")
csN1 <- process(qn1, verbose=verbose) #time required to process CEL sets
plm1.merge <- RmaPlm(expo_full_set_bc_processed,flavor="affyPLM")
fit(plm1.merge, ram=5,verbose=verbose) #time required
chp1.merge<-getChipEffectSet(plm1.merge)
data.aroma <- extractDataFrame(chp1.merge, addNames=TRUE)
#take away the at sign at the end of the id names
rownames(data.aroma) <- sub("_at", "",as.character(data.aroma$unitName))
Cheers,
John Patrick
Turku University Finland
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