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="three-five-and-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 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)
            N1       N2       N3       A1       A2       A3
slope  2.3e+00 2.21e+00 2.56e+00 5.38e+00 4.32e+00 5.68e+00
pvalue 3.9e-08 8.13e-08 3.12e-09 9.03e-12 4.91e-10 5.35e-12
> 
> 
> ## probe level models can show up more subtle artifacts
> library( "affyPLM" )
> Pset1 <- fitPLM( AmpData )
> show( Pset1 )
Probe level linear model (PLMset) object
size of arrays=712x712
cdf=HG-U133A (22283 probeset ids)
number of samples=6
number of probesets=22283
number of chip level parameters for each probeset=6
annotation=hgu133a
PLMset settings
Creating function: fitPLM 
Preprocessing
Background Correction=TRUE Method= RMA.2
Normalization=TRUE Method= quantile

Model/Summarization
$constraint.type
          default 
"contr.treatment" 

$variable.type
 default 
"factor" 

$model.param
$model.param$trans.fn
[1] "log2"

$model.param$se.type
[1] 4

$model.param$psi.type
[1] 0

$model.param$psi.k
[1] 1.345

$model.param$max.its
[1] 20

$model.param$init.method
[1] "ls"

$model.param$weights.chip
NULL

$model.param$weights.probe
NULL



Output Settings
$weights
[1] TRUE

$residuals
[1] TRUE

$varcov
[1] "none"

$resid.SE
[1] TRUE

> 
> ## 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)
> 
> 
> warnings()
NULL
>