data(allQC)Scale FactorPercent PresentAverage BackgroundMinimum BackgroundMaximum BackgroundBioBBioCBioDCreXAFFX-HSAC07/X00351.3'/5'AFFX-HUMGAPDH/M33197.3'/5'
library("affy")## Get the raw data (see help("MLL.B") for further details) library("ALLMLL") data(MLL.B)
## Generate the QC metrics library("simpleaffy") data.all <- MLL.B[,1:20] all.qc <- qc(data.all)
## Select relevant information allQC <- cbind(sfs(all.qc),percent.present(all.qc)/100, avbg(all.qc),minbg(all.qc),maxbg(all.qc), spikeInProbes(all.qc),ratios(all.qc)[,c(1,3)]) ## Specify row and column names colnames(allQC) <- c("Scale Factor","Percent Present", "Average Background", "Minimum Background", "Maximum Background", "BioB", "BioC", "BioD", "CreX", "AFFX-HSAC07/X00351.3'/5'", "AFFX-HUMGAPDH/M33197.3'/5'") rownames(allQC) <- 1:20 Versions 1.16.0 of affy, 1.2.2 of ALLMLL, and 2.14.05 of simpleaffy was used. Part of this dataset has been also studied by Bolstad et al. (2005) and Brettschneider et al. (2007).