normalizeBetweenArrays {limma} | R Documentation |
Normalizes expression intensities so that the intensities or log-ratios have similar distributions across a series of arrays.
normalizeBetweenArrays(object, method, targets=NULL, ...)
object |
an matrix or MAList object containing expression ratios for a series of arrays |
method |
character string specifying the normalization method to be used.
Choices are "none" , "scale" , "quantile" , "Aquantile" , "Gquantile" , "Rquantile" , "Tquantile" or "vsn" .
A partial string sufficient to uniquely identify the choice is permitted. |
targets |
vector, factor or matrix of length twice the number of arrays, used to indicate target groups if method="Tquantile" |
... |
other arguments are passed to normalizeQuantiles if one of the quantile methods are used or to vsn if method="vsn" |
normalizeWithinArrays
normalizes expression values to make intensities consistent within each array.
normalizeBetweenArrays
normalizes expression values to achieve consistency between arrays.
The scale normalization method was proposed by Yang et al (2001, 2002) and is further explained by Smyth and Speed (2003).
The idea is simply to scale the log-ratios to have the same median-abolute-deviation (MAD) across arrays.
This idea has also been implemented by the maNormScale
function in the marrayNorm package.
The implementation here is slightly different in that the MAD scale estimator is replaced with the median-absolute-value and the A-values are normalized as well as the M-values.
Quantile normalization was proposed by Bolstad et al (2003) for Affymetrix-style single-channel arrays and by Yang and Thorne (2003) for two-color cDNA arrays.
method="quantile"
ensures that the intensities have the same empirical distribution across arrays and across channels.
method="Aquantile"
ensures that the A-values (average intensities) have the same empirical distribution across arrays leaving the M-values (log-ratios) unchanged.
These two methods are called "q" and "Aq" respectively in Yang and Thorne (2003).
method="Tquantile"
performs quantile normalization separately for the groups indicated by targets
.
targets
may be a target matrix such as read by readTargets
or can be a vector indicating green channel groups followed by red channel groups.
method="Gquantile"
ensures that the green (first) channel has the same empirical distribution across arrays, leaving the M-values (log-ratios) unchanged.
method="Rquantile"
ensures that the red (second) channel has the same empirical distribution across arrays, leaving the M-values (log-ratios) unchanged.
method="vsn"
uses the vsn
function from the vsn package.
The normalized intensities are converted to the log-2 scale.
If object
is a matrix
then the scale, quantile or vsn normalization will be applied to the columns.
Applying method="Aquantile"
when object
is a matrix
will produce an error.
If object
is a matrix then normalizeBetweenArrays
produces a matrix of the same size.
Otherwise, normalizeBetweenArrays
produces an MAList
object with M and A-values on the log-2 scale.
Gordon Smyth
Bolstad, B. M., Irizarry R. A., Astrand, M., and Speed, T. P. (2003), A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19, 185-193.
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.
Yang, Y. H., Dudoit, S., Luu, P., and Speed, T. P. (2001). Normalization for cDNA microarray data. In Microarrays: Optical Technologies and Informatics, M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), Proceedings of SPIE, Volume 4266, pp. 141-152.
Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J., and Speed, T. P. (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30(4):e15.
Yang, Y. H., and Thorne, N. P. (2003). Normalization for two-color cDNA microarray data. In: D. R. Goldstein (ed.), Science and Statistics: A Festschrift for Terry Speed, IMS Lecture Notes - Monograph Series, Volume 40, pp. 403-418.
An overview of LIMMA functions for normalization is given in 4.Normalization.
See also maNormScale
in the marrayNorm package, normalize
in the affy package and vsn
in the vsn package.
library(sma) data(MouseArray) MA <- normalizeWithinArrays(mouse.data, mouse.setup) plot.scale.box(MA$M) # Between array scale normalization as in Yang et al (2001): MA <- normalizeBetweenArrays(MA,method="scale") print(MA) show(MA) plot.scale.box(MA$M) # One can get the same results using the matrix method: M <- normalizeBetweenArrays(MA$M,method="scale") plot.scale.box(M) # MpAq normalization as in Yang and Thorne (2003): MpAq <- normalizeWithinArrays(mouse.data, mouse.setup) MpAq <- normalizeBetweenArrays(MpAq, method="Aq") plotDensities(MpAq)