## 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)