quantileNormalize {HELP} | R Documentation |
Quantile normalization
Description
Apply quantile normalization to multiple bins of data, divided by a sliding window approach
Usage
quantileNormalize(x, y, ...)
Arguments
x |
the vector of numerical data to be normalized. If x is a matrix it is interpreted as a vector. x can also be of class "ExpressionSet" .
|
y |
an additional vector of numerical data to be used for binning. If y is a matrix it is interpreted as a vector. y can also be of class "ExpressionSet" .
|
... |
Arguments to be passed to methods (see
quantileNormalize-methods ):
- element
which element of AssayData to use for a given ExpressionSet input (default is "exprs")
- sample
which element of sampleNames to use as data (default is 1). Can be a character matching a sample name or simply an integer indicating which sample to choose. See getSamples .
- feature
which element of featureData to use as binning variable (default is 1). Can be a character matching varLabel or simply an integer indicating which feature to choose. See getFeatures .
- num.bins
number of bins (default is 10) used to divide the data
- num.steps
number of steps (default is 3) used to create bin offsets, resulting in bins of sliding windows
- mode
the binning mode to be used. This must be either "continuous" (default) or "discrete". "continuous" mode will divide the data into density-dependent bins. "discrete" mode will divide the data uniformly by binning data values.
- type
an integer between 1 and 9 (default is 7) selecting one of the nine quantile algorithms: see quantile .
- na.rm
logical; if TRUE, missing values are removed from x and y. If FALSE any missing values cause an error.
- \dots
other arguments to be passed to quantile . See quantile .
|
Value
Returns a vector of normalized numerical data according to input parameters.
Author(s)
Reid F. Thompson (rthompso@aecom.yu.edu)
See Also
quantileNormalize-methods
, quantile
Examples
#demo(pipeline,package="HELP")
x <- rep(1:100,10)+10*rep(1:10,each=100)
y <- rep(1:20,each=50)
d <- density(quantileNormalize(x,y,num.bins=20,num.steps=1,mode="discrete"))
plot(density(x))
lines(d$x,d$y/3,col="red")
#rm(x,y,d)
[Package
HELP version 1.40.0
Index]