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 1.0.0 Index]