## 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 use a different data set for this part. The original location is not
## attributed here, so I don't know where this data comes from.
library( "AmpAffyExample" )
data( AmpData )

## RNA Degregation -- unfortunately, this varies a bit from chip to chip, so
## there are fewer general rules about what is okay, and what is not.
sampleNames(AmpData) <- c("N1", "N2", "N3", "A1", "A2", "A3" )
RNAdeg <- AffyRNAdeg(AmpData)

plotAffyRNAdeg(RNAdeg,col=c(2,2,2,3,3,3))
summaryAffyRNAdeg(RNAdeg)


## probe level models can show up more subtle artifacts
library( "affyPLM" )
Pset1 <- fitPLM( AmpData )
show( Pset1 )

## this one shows a chip with a ring in the middle.
par(mfrow = c(2,2))
image(AmpData[,3])
image(Pset1,type="weights",which=3)
image(Pset1,type="resids",which=3)
image(Pset1,type="sign.resids",which=3)