4.Normalization {limma} | R Documentation |
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 normalizeMedians
, normalizeMedianDeviations
and normalizeQuantiles
, none of which need to be called directly by users.
Usually one doesn't need to explicitly ask for background correction of the intensities because this is done by default by normalizeWithinArrays
,
which subtracts the background from the foreground intensities before applying the normalization method.
This default background correction method can be over-ridden by using backgroundCorrect
which offers a number of alternative
background correct methods to simple subtraction.
Simply use backgroundCorrect
to correct the RGList
before applying normalizeWithinArrays
.
backgroundCorrect
uses utility functions ma3x3.matrix
and ma3x3.spottedarray
.
kooperberg
is a Bayesian background correction tool designed specifically for GenePix data.
kooperberg
is not currently used as the default method for GenePix data because it is computationally intensive.
It requires several columns of the Genepix data files which are not read in by read.maimages, so you will need to use read.series
instead of read.maimages
if you wish to use kooperberg
.
Gordon Smyth
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