Description Usage Arguments Details Value Note Author(s) References See Also Examples
Performs a sequence of pre-processing 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 sub-bead pool for each bead-type. See
|
normOpts |
List output from |
... |
Further arguments to |
plotArray |
Numeric index to a single array to plot |
Using setNormOptions
, default pre-processing 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
non-signal 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 noise-parameterisation,
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 1-norm “minkowski”) distance is the best option for polar coordinates. If the three points fall on a circle, the “euclidean” (or 2-norm “minkowski”) distance is the best option. If the rubber band forms a shape intermediate between a circle and a square (e.g. 4th-root transformation), the 5-norm “minkowski” distance or similar may the best choice.
The function preprocessBeadSet
calls several pre-processing
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 pre-processing
options
The function plotPreprocessing
is used for its side effects
Output from preprocessIllumina
is a
"BeadSetIllumina"
object with pre-processed
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 31:265-27
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 BeadSetIllumina-object
rPath <- system.file("extdata", package="beadarrayMSV")
BSDataRaw <- readBeadSummaryOutput(path=rPath,recursive=TRUE)
#Find indexes to sub-bead 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))
#Pre-process
normOpts <- setNormOptions(minSize=10)
plotPreprocessing(BSDataRaw,normInd,normOpts,plotArray=1)
BSData <- preprocessBeadSet(BSDataRaw,normInd,normOpts)
pData(BSData)
## End(Not run)
|
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