normalizeAffine: Weighted affine normalization between channels and arrays

Description Usage Arguments Details Value Negative, non-positive, and saturated values Missing values Weighted normalization Robustness Using known/previously estimated channel offsets Author(s) References See Also Examples

Description

Weighted affine normalization between channels and arrays.

This method will remove curvature in the M vs A plots that are due to an affine transformation of the data. In other words, if there are (small or large) biases in the different (red or green) channels, biases that can be equal too, you will get curvature in the M vs A plots and this type of curvature will be removed by this normalization method.

Moreover, if you normalize all slides at once, this method will also bring the signals on the same scale such that the log-ratios for different slides are comparable. Thus, do not normalize the scale of the log-ratios between slides afterward.

It is recommended to normalize as many slides as possible in one run. The result is that if creating log-ratios between any channels and any slides, they will contain as little curvature as possible.

Furthermore, since the relative scale between any two channels on any two slides will be one if one normalizes all slides (and channels) at once it is possible to add or multiply with the same constant to all channels/arrays without introducing curvature. Thus, it is easy to rescale the data afterwards as demonstrated in the example.

Usage

1
2
3
## S3 method for class 'matrix'
normalizeAffine(X, weights=NULL, typeOfWeights=c("datapoint"), method="L1",
  constraint=0.05, satSignal=2^16 - 1, ..., .fitOnly=FALSE)

Arguments

X

An NxK matrix (K>=2) where the columns represent the channels, to be normalized.

weights

If NULL, non-weighted normalization is done. If data-point weights are used, this should be a vector of length N of data point weights used when estimating the normalization function.

typeOfWeights

A character string specifying the type of weights given in argument weights.

method

A character string specifying how the estimates are robustified. See iwpca() for all accepted values.

constraint

Constraint making the bias parameters identifiable. See fitIWPCA() for more details.

satSignal

Signals equal to or above this threshold will not be used in the fitting.

...

Other arguments passed to fitIWPCA() and in turn iwpca(). For example, the weight argument of iwpca(). See also below.

.fitOnly

If TRUE, the data will not be back-transform.

Details

A line is fitted robustly throught the (y_R,y_G) observations using an iterated re-weighted principal component analysis (IWPCA), which minimized the residuals that are orthogonal to the fitted line. Each observation is down-weighted by the inverse of the absolute residuals, i.e. the fit is done in L_1.

Value

A NxK matrix of the normalized channels. The fitted model is returned as attribute modelFit.

Negative, non-positive, and saturated values

Affine normalization applies equally well to negative values. Thus, contrary to normalization methods applied to log-ratios, such as curve-fit normalization methods, affine normalization, will not set these to NA.

Data points that are saturated in one or more channels are not used to estimate the normalization function, but they are normalized.

Missing values

The estimation of the affine normalization function will only be made based on complete non-saturated observations, i.e. observations that contains no NA values nor saturated values as defined by satSignal.

Weighted normalization

Each data point/observation, that is, each row in X, which is a vector of length K, can be assigned a weight in [0,1] specifying how much it should affect the fitting of the affine normalization function. Weights are given by argument weights, which should be a numeric vector of length N. Regardless of weights, all data points are normalized based on the fitted normalization function.

Robustness

By default, the model fit of affine normalization is done in L_1 (method="L1"). This way, outliers affect the parameter estimates less than ordinary least-square methods.

For further robustness, downweight outliers such as saturated signals, if possible.

We do not use Tukey's biweight function for reasons similar to those outlined in calibrateMultiscan().

Using known/previously estimated channel offsets

If the channel offsets can be assumed to be known, then it is possible to fit the affine model with no (zero) offset, which formally is a linear (proportional) model, by specifying argument center=FALSE. In order to do this, the channel offsets have to be subtracted from the signals manually before normalizing, e.g. Xa <- t(t(X)-a) where e is vector of length ncol(X). Then normalize by Xn <- normalizeAffine(Xa, center=FALSE). You can assert that the model is fitted without offset by stopifnot(all(attr(Xn, "modelFit")$adiag == 0)).

Author(s)

Henrik Bengtsson

References

[1] Henrik Bengtsson and Ola Hössjer, Methodological Study of Affine Transformations of Gene Expression Data, Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method, BMC Bioinformatics, 2006, 7:100.

See Also

calibrateMultiscan().

Examples

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light")
rg <- read.table(pathname, header=TRUE, sep="\t")
nbrOfScans <- max(rg$slide)

rg <- as.list(rg)
for (field in c("R", "G"))
  rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans)
rg$slide <- rg$spot <- NULL
rg <- as.matrix(as.data.frame(rg))
colnames(rg) <- rep(c("R", "G"), each=nbrOfScans)

layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE))

rgC <- rg
for (channel in c("R", "G")) {
  sidx <- which(colnames(rg) == channel)
  channelColor <- switch(channel, R="red", G="green")

  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  # The raw data
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  plotMvsAPairs(rg[,sidx])
  title(main=paste("Observed", channel))
  box(col=channelColor)
 
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  # The calibrated data
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL)

  plotMvsAPairs(rgC[,sidx])
  title(main=paste("Calibrated", channel))
  box(col=channelColor)
} # for (channel ...)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# The average calibrated data
#
# Note how the red signals are weaker than the green. The reason
# for this can be that the scale factor in the green channel is
# greater than in the red channel, but it can also be that there
# is a remaining relative difference in bias between the green
# and the red channel, a bias that precedes the scanning.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
rgCA <- rg
for (channel in c("R", "G")) {
  sidx <- which(colnames(rg) == channel)
  rgCA[,sidx] <- calibrateMultiscan(rg[,sidx])
}

rgCAavg <- matrix(NA_real_, nrow=nrow(rgCA), ncol=2)
colnames(rgCAavg) <- c("R", "G")
for (channel in c("R", "G")) {
  sidx <- which(colnames(rg) == channel)
  rgCAavg[,channel] <- apply(rgCA[,sidx], MARGIN=1, FUN=median, na.rm=TRUE)
}

# Add some "fake" outliers
outliers <- 1:600
rgCAavg[outliers,"G"] <- 50000

plotMvsA(rgCAavg)
title(main="Average calibrated (AC)")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Normalize data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Weight-down outliers when normalizing
weights <- rep(1, nrow(rgCAavg))
weights[outliers] <- 0.001

# Affine normalization of channels
rgCANa <- normalizeAffine(rgCAavg, weights=weights)
# It is always ok to rescale the affine normalized data if its
# done on (R,G); not on (A,M)! However, this is only needed for
# esthetic purposes.
rgCANa <- rgCANa *2^1.4
plotMvsA(rgCANa)
title(main="Normalized AC")

# Curve-fit (lowess) normalization
rgCANlw <- normalizeLowess(rgCAavg, weights=weights)
plotMvsA(rgCANlw, col="orange", add=TRUE)

# Curve-fit (loess) normalization
rgCANl <- normalizeLoess(rgCAavg, weights=weights)
plotMvsA(rgCANl, col="red", add=TRUE)

# Curve-fit (robust spline) normalization
rgCANrs <- normalizeRobustSpline(rgCAavg, weights=weights)
plotMvsA(rgCANrs, col="blue", add=TRUE)

legend(x=0,y=16, legend=c("affine", "lowess", "loess", "r. spline"), pch=19,
       col=c("black", "orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n")


plotMvsMPairs(cbind(rgCANa, rgCANlw), col="orange", xlab=expression(M[affine]))
title(main="Normalized AC")
plotMvsMPairs(cbind(rgCANa, rgCANl), col="red", add=TRUE)
plotMvsMPairs(cbind(rgCANa, rgCANrs), col="blue", add=TRUE)
abline(a=0, b=1, lty=2)
legend(x=-6,y=6, legend=c("lowess", "loess", "r. spline"), pch=19,
       col=c("orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n")

aroma.light documentation built on May 2, 2018, 2:52 a.m.