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NanoStringDataNormalization <- function(path=path, header=TRUE, designs){
# ------------------------------------ #
# path: points out the directory in which your csv.file is located #
# header: a logical value(TRUE or FALSE) indicating whether the file contains the names of the variables as its first line. #
# designs: data frame for pheno type data storage #
# ------------------------------------ #
NanoStringData <- createNanoStringSetFromCsv(path, header, designs) # Create a NanoStringSet needed in NanoStringDiff from csv file #
NanoStringData <- estNormalizationFactors(NanoStringData) # get normalization factors #
PosFactor <- positiveFactor(NanoStringData) # get positive size factor #
HouseFactor <- housekeepingFactor(NanoStringData) # get housekeeping size factor #
NegFactor <- negativeFactor(NanoStringData) # get background niose #
RawData <- exprs(NanoStringData) # get raw data #
NormalizedData <- round((RawData - NegFactor)/(PosFactor * HouseFactor)) # normalization #
NormalizedData[NormalizedData < 0] <- 0 # set negative value as 0 #
ColName <- colnames(NormalizedData)
colnames(NormalizedData) <- paste("Normalized", ColName, sep = "") # change column for normalized data #
return(list(RawData = RawData, NormalizedData = NormalizedData))
} # end function #
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