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The function is based on unpublished work by the author.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 05.Normalization.

### Examples

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

[Package

*limma* version 2.4.7

Index]