normalizeQuantileRank.matrix: Normalizes the empirical distribution of a set of samples to...
In aroma.light: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

Description Usage Arguments Value Missing values Weights Author(s) See Also Examples

Normalizes the empirical distribution of a set of samples to a common target distribution.

The average sample distribution is calculated either robustly or not by utilizing either weightedMedian() or weighted.mean(). A weighted method is used if any of the weights are different from one.

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## S3 method for class 'matrix'
normalizeQuantileRank(X, ties=FALSE, robust=FALSE, weights=NULL,
  typeOfWeights=c("channel", "signal"), ...)

`X`	a numerical NxK `matrix` with the K columns representing the channels and the N rows representing the data points.
`robust`	If `TRUE`, the (weighted) median function is used for calculating the average sample distribution, otherwise the (weighted) mean function is used.
`ties`	Should ties in `x` be treated with care or not? For more details, see "limma:normalizeQuantiles".
`weights`	If `NULL`, non-weighted normalization is done. If channel weights, this should be a `vector` of length K specifying the weights for each channel. If signal weights, it should be an NxK `matrix` specifying the weights for each signal.
`typeOfWeights`	A `character` string specifying the type of weights given in argument `weights`.
`...`	Not used.

Returns an object of the same shape as the input argument.

Missing values are excluded when estimating the "common" (the baseline). Values that are NA remain NA after normalization. No new NAs are introduced.

Currently only channel weights are support due to the way quantile normalization is done. If signal weights are given, channel weights are calculated from these by taking the mean of the signal weights in each channel.

Adopted from Gordon Smyth (http://www.statsci.org/) in 2002 \& 2006. Original code by Ben Bolstad at Statistics Department, University of California. Support for calculating the average sample distribution using (weighted) mean or median was added by Henrik Bengtsson.

median, weightedMedian, mean() and weighted.mean. normalizeQuantileSpline().

# Simulate three samples with on average 20% missing values
N <- 10000
X <- cbind(rnorm(N, mean=3, sd=1),
           rnorm(N, mean=4, sd=2),
           rgamma(N, shape=2, rate=1))
X[sample(3*N, size=0.20*3*N)] <- NA

# Normalize quantiles
Xn <- normalizeQuantile(X)

# Plot the data
layout(matrix(1:2, ncol=1))
xlim <- range(X, Xn, na.rm=TRUE)
plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions")
plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions")