The following is a real data file and contains four different types of gene expression measurements (including RPKM) from one most recent finished RNAseq data. Find the file path from your Linux machine or rhdna account and load the data directly into R. Then select RPKM measurements for all samples. After excluding genes that no samples have RP…

qnormdata.R

http://156.40.185.32/pub/human.Hela.ALDH.ethanol.RNAseq/features/allHela.sorted.wig.exon.high50Ave.peakFeats.allChr.exonLen.subTotal.RPKM.highest_in_gene.transposed.txt.gz
README file: http://156.40.185.32/pub/human.Hela.ALDH.ethanol.RNAseq/README.human.Hela.ALDH.ethanol.RNAseq.txt
 
dataFile = "/mnt/xdat/pub/human.Hela.ALDH.ethanol.RNAseq/features/allHela.sorted.wig.exon.high50Ave.peakFeats.allChr.exonLen.subTotal.RPKM.highest_in_gene.transposed.txt.gz"
rawdata = read.delim(gzfile(dataFile), sep="\t", header=T)
rownames(rawdata) = paste(rawdata[,"Chr"],  rawdata[,"GeneSym"], sep = ".")
data.RPKM = rawdata[, grep("RPKM", colnames(rawdata))]
rowMax = do.call("pmax", c(data.RPKM[,1:ncol(data.RPKM)],na.rm=TRUE))
data.RPKM.rowMax.gt1 = data.RPKM[rowMax >1,]
data.RPKM.rowMax.gt1[is.na(data.RPKM.rowMax.gt1)] = 0
data.RPKM.log2 = log2(data.RPKM.rowMax.gt1 + 1)
RNAseq.RPKM.log.m = as.matrix(data.RPKM.log2)
library(limma)
library(affy)
RNAseq.RPKM.log.m.qNorm = normalizeBetweenArrays(RNAseq.RPKM.log.m, method="quantile")