Description Usage Arguments Details Value Note Author(s) References See Also Examples
Performs a sequence of preprocessing routines on objects of class
"BeadSetIllumina"
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  setNormOptions(shearInf1 = TRUE, transf = "root",
method = "medianAF",
minSize = suggestSh(shearInf1)$minSize,
prob = suggestSh(shearInf1)$prob,
nBins = suggestSh(shearInf1)$nBins,
dist = suggestTr(transf)$dist,
pNorm = suggestTr(transf)$pNorm,
nthRoot = suggestTr(transf)$nthRoot,
offset = suggestTr(transf)$offset,
scale = suggestNo(method)$scale,
nSD = 3, breaks = 200)
plotPreprocessing(BSData, normInd,
normOpts = setNormOptions(shearInf1 = !is.null(normInd)),
plotArray = 1, ...)
preprocessBeadSet(BSData, normInd,
normOpts = setNormOptions(shearInf1 = !is.null(normInd)))

shearInf1 
If 
transf 
Character string denoting transformation. One of “none”,
“log” (base 2), or “root” (defined by 
method 
Character string denoting channel normalization method for each
array. One of “none”, “quantNorm”, “medianAF”,
or “linPeak”. For quantile normalization, the
limma package is required (Smyth and Speed, 2003). For “medianAF”, the
red channel is scaled such that 
minSize 
The homozygote asymptotes are found by drawing a straight line
through quantile points distributed in bins along each axis. Only
bins containing more than 
prob 
Numeric probabiliy used in the 
nBins 
The number of bins into which to divide the points along each axis before the homozygote asymptotes are drawn 
dist 
Character string defining the distance measure used for polar
coordinates transformation of the signal. One of “manhattan”,
“euclidean”, or “minkowski”. See 
pNorm 
See 
nthRoot 
Numeric used together with 
offset 
A numeric offset added to each channel before transformation. Values
below zero are set to 
scale 
Used with 
nSD 
The background signal is estimated as 
breaks 
The parameterisation of noise levels is based on a histogram of each
channel, where the numeric 
BSData 

normInd 
Matrix with logical indexes to subbead pool for each beadtype. See

normOpts 
List output from 
... 
Further arguments to 
plotArray 
Numeric index to a single array to plot 
Using setNormOptions
, default preprocessing options are
suggested, and any changes may be specified. The effects of
different options are studied using plotPreprocessing
for a
number of arbitrary arrays. This produces four plots; i) raw data
scatter, ii) scatter including the estimated asymptotes for the affine
transformation (red/green) including the quantile points used (blue
dots), iii) the noise levels for the red and green channel after
transformation, parameterized signal superimposed, based on the
nonsignal channels of Infinium I beads, and iv) scatter after
transformation including new axes (green) and estimated noise levels
(red dots).
For the affine transformation, it is important that enough quantile
points are included to get reliable asymptotes. If there are few blue
dots in plot ii), decrease the minSize
option or set
shearInf1
to FALSE
. If the grey lines in plot iii) are
too coarse (too few points) to get a good noiseparameterisation,
increase breaks
. Note also how the noise levels are affected by
different transformations.
Pay close regard to how the transformation affects the shapes of the clouds in plot iv). Ideally, three well defined clouds protrude from the estimated origin, corresponding to the homozygotes which fall on the estimated axes and the heterozygotes which fall 45 degrees in between. Imagine a rubber band stretched over the ends of the three clouds. If the rubber band is straight (no transformation), the “manhattan” (or 1norm “minkowski”) distance is the best option for polar coordinates. If the three points fall on a circle, the “euclidean” (or 2norm “minkowski”) distance is the best option. If the rubber band forms a shape intermediate between a circle and a square (e.g. 4throot transformation), the 5norm “minkowski” distance or similar may the best choice.
The function preprocessBeadSet
calls several preprocessing
routines in sequence. First shearRawSignal
performs
the affine transformations, then getNoiseDistributions
estimates the distributions of the noise for each channel. Next,
transformChannels
transforms the signal, followed by
transformation of the standard errors of each channel using
transformSEs
. In the end,
normalizeShearedChannels
performs channel
normalisation for each array.
Output from setNormOptions
is a list with preprocessing
options
The function plotPreprocessing
is used for its side effects
Output from preprocessIllumina
is a
"BeadSetIllumina"
object with preprocessed
assayData
entries. A column “noiseIntensity” is added to
phenoData
, this is the (parameterized) standard error times
nSD
If BSData
contains a phenoData
column
“noiseIntensity”, preprocessBeadSet
assumes the data are
already normalized and an error is produced
Lars Gidskehaug
G. K. Smyth and T. P. Speed. (2003) Normalization of cDNA microarray data. Methods 31:26527
readBeadSummaryOutput
, getNormInd
,
shearRawSignal
, getNoiseDistributions
,
transformChannels
, transformSEs
,
normalizeShearedChannels
, createAlleleSet
,
BeadSetIllumina
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## Not run:
#Read files into BeadSetIlluminaobject
rPath < system.file("extdata", package="beadarrayMSV")
BSDataRaw < readBeadSummaryOutput(path=rPath,recursive=TRUE)
#Find indexes to subbead pools
beadInfo < read.table(paste(rPath,'beadData.txt',sep='/'),sep='\t',
header=TRUE,as.is=TRUE)
rownames(beadInfo) < make.names(beadInfo$Name)
normInd < getNormInd(beadInfo,featureNames(BSDataRaw))
#Preprocess
normOpts < setNormOptions(minSize=10)
plotPreprocessing(BSDataRaw,normInd,normOpts,plotArray=1)
BSData < preprocessBeadSet(BSDataRaw,normInd,normOpts)
pData(BSData)
## End(Not run)

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