05.Normalization {limma} | R Documentation

## Normalization of Microarray Data

### Description

This page gives an overview of the LIMMA functions available to normalize data from spotted two-colour microarrays. Smyth and Speed (2003) give an overview of the normalization techniques implemented in the functions.

Usually data from spotted microarrays will be normalized using `normalizeWithinArrays`. A minority of data will also be normalized using `normalizeBetweenArrays` if diagnostic plots suggest a difference in scale between the arrays.

In rare circumstances, data might be normalized using `normalizeForPrintorder` before using `normalizeWithinArrays`.

All the normalization routines take account of spot quality weights which might be set in the data objects. The weights can be temporarily modified using `modifyWeights` to, for example, remove ratio control spots from the normalization process.

If one is planning analysis of single-channel information from the microarrays rather than analysis of differential expression based on log-ratios, then the data should be normalized using a single channel-normalization technique. Single channel normalization uses further options of the `normalizeBetweenArrays` function. For more details see the LIMMA User's Guide which includes a section on single-channel normalization.

`normalizeWithinArrays` uses utility functions `MA.RG`, `loessFit` and `normalizeRobustSpline`. `normalizeBetweenArrays` uses utility functions `normalizeMedianAbsValues` and `normalizeQuantiles`, none of which need to be called directly by users.

### Author(s)

Gordon Smyth

### References

Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. In: METHODS: Selecting Candidate Genes from DNA Array Screens: Application to Neuroscience, D. Carter (ed.). Methods Volume 31, Issue 4, December 2003, pages 265-273. http://www.statsci.org/smyth/pubs/normalize.pdf

[Package limma version 2.12.0 Index]