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. ... 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 1.2.0 Index]