normalizeRobustSpline {limma} | R Documentation

## Normalize Single Microarray Using Shrunk Robust Splines

### Description

Normalize the M-values for a single microarray using robustly fitted regression splines and empirical Bayes shrinkage.

### Usage

    normalizeRobustSpline(M,A,layout,df=5,method="M")

### Arguments

`M` | numeric vector of M-values
`A` | numeric vector of A-values
`layout` | list specifying the dimensions of the spot matrix and the grid matrix
`df` | degrees of freedom for regression spline, i.e., the number of regression coefficients and the number of knots
`method` | choices are `"M"` for M-estimation or `"MM"` for high breakdown point regression

### Details

This function implements an idea similar to print-tip loess normalization but uses regression splines in place of the loess curves and uses empirical Bayes ideas to shrink the individual prtin-tip curves towards a common value. This allows the technique to introduce less noise into good quality arrays with little spatial variation while still giving good results on arrays with strong spatial variation.

### Value

Numeric vector containing normalized M-values.

### Author(s)

Gordon Smyth

### References

The function is based on unpublished work by the author.

### See Also

An overview of LIMMA functions for normalization is given in 4.Normalization.

### Examples

    library(sma)
    data(MouseArray)
    M <- m.spot(mouse1)
    A <- a.spot(mouse1)
    M <- normalizeRobustSpline(M,A,mouse.setup)

[Package limma version 1.9.6 Index]