R version 2.9.2 (2009-08-24)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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 - MKL libraries for acclerated math installed: see help (setMKLthreads)
 - ParallelR packages installed: see help (package='foreach')

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> png(filename="more-plm.r%02d.png")
> 
> ## we need to load some libraries and do some preparation on the initial data
> ## sets. the affy library is a set of standard analysis routines. while ALLMLL
> ## contains the data reported at Mary E. Ross, Xiaodong Zhou, Guangchun Song,
> ## Sheila A. Shurtleff, Kevin Girtman, W. Kent Williams, Hsi-Che Liu, Rami
> ## Mahfouz, Susana C. Raimondi, Noel Lenny, Anami Patel, and James R. Downing
> ## (2003) Classification of pediatric acute lymphoblastic leukemia by gene
> ## expression profiling Blood 102: 2951-2959
> library( "affy" )
> library( "ALLMLL" )
> 
> 
> data( MLL.B )
> Data <- MLL.B[, c(2,1,3:5,14,6,13)]
> sampleNames(Data) <- letters[1:8]
> 
> 
> ## we need to load some libraries and do some preparation on the initial data
> ## sets. the affy library is a set of standard analysis routines. while ALLMLL
> ## contains the data reported at Mary E. Ross, Xiaodong Zhou, Guangchun Song,
> ## Sheila A. Shurtleff, Kevin Girtman, W. Kent Williams, Hsi-Che Liu, Rami
> ## Mahfouz, Susana C. Raimondi, Noel Lenny, Anami Patel, and James R. Downing
> ## (2003) Classification of pediatric acute lymphoblastic leukemia by gene
> ## expression profiling Blood 102: 2951-2959
> library( "affy" )
> library( "ALLMLL" )
> 
> 
> data( MLL.B )
> Data <- MLL.B[, c(2,1,3:5,14,6,13)]
> sampleNames(Data) <- letters[1:8]
> 
> 
> ## Now that we have prepared the data, let's try looking at some of it. First,
> ## set up the visualisation
> palette.gray <- c(rep(gray(0:10/10), times = seq(1,41,by=4)))
> par(mfrow=c(1,2))
> ## now view the data, one gray scale, the other on a log scale. You should see
> ## that this chip has a relatively strong spatial artifact, or as it is more
> ## technically known, a light bit down the side.
> image(Data[,1], transfo=function(x) x, col=palette.gray)
> image(Data[,1], col = palette.gray)
> 
> 
> ## Next we can consider the distribution of the intensities of the probes with
> ## a box plot, as well as probe level data.
> 
> ## The practical outcome here is that the chip marked "f" is a bit suspicious,
> ## being a long way of the range of the other chips. Chip "a" has a biomodal
> ## distribution, which is probably a spatial artifact.
> library( "RColorBrewer" )
> cols <- brewer.pal(8, "Set1")
> boxplot(Data, col = cols)
> 
> hist(Data, col=cols, lty = 1, xlab="Log (base 2) intensities")
> legend(12, 1, letters[1:8],lty=1,col=cols)
> 
> ## and scatter plots -- again, f, is an outlier.
> par(mfrow = c(2,4))
> MAplot(Data,cex=0.75)
> mtext( "M", 2, outer=TRUE)
> mtext( "A", 1, outer=TRUE)
> 
> 
> ## and finally some more PLM data on the original data set.
> library( "affyPLM" )
> Pset2 <- fitPLM(MLL.B)
> Mbox( Pset2, ylim=c(-1,1), col = cols,
+      names = NULL, main="RLE")
> 
> boxplot(Pset2, ylim=c(0.95,1.5), col=cols,
+         names=NULL,main="NUSE",outline=FALSE)
> 
> 
> warnings()
NULL
>