normalizeWithinArrays {limma}R Documentation

Normalize Within Arrays


Normalize the expression log-ratios for one or more two-colour spotted microarray experiments so that the log-ratios average to zero within each array or sub-array.


normalizeWithinArrays(object, layout, method="printtiploess", weights=object$weights, span=0.3, iterations=4, controlspots=NULL, df=5, robust="M", bc.method="subtract", offset=0)
MA.RG(object, bc.method="subtract", offset=0)


object object of class list, RGList or MAList containing two-color microarray data
layout list specifying the dimensions of the spot matrix and the grid matrix. For details see PrintLayout-class.
method character string specifying the normalization method. Choices are "none", "median", "loess", "printtiploess", "composite", "control" and "robustspline". A partial string sufficient to uniquely identify the choice is permitted.
weights numeric matrix or vector of the same size and shape as the components of object. Will use by default weights found in object if they exist.
span numeric scalar giving the smoothing parameter for the loess fit
iterations number of iterations used in loess fitting. More iterations give a more robust fit.
controlspots numeric or logical vector specifying the subset of spots which are non-differentially expressed control spots, for use with method="composite"
df degrees of freedom for spline if method="robustspline"
robust robust regression method if method="robustspline". Choices are "M" or "MM".
bc.method character string specifying background correct method, see backgroundCorrect for options
offset numeric value, intensity offset used when computing log-ratios, see backgroundCorrect


Normalization is intended to remove from the expression measures any systematic trends which arise from the microarray technology rather than from differences between the probes or between the target RNA samples hybridized to the arrays.

This function normalizes M-values (log-ratios) for dye-bias within each array. Apart from method="none" and method="median", all the normalization methods make use of the relationship between dye-bias and intensity. The loess normalization methods were proposed by Yang et al (2001, 2002). Smyth and Speed (2003) give a detailed statement of the methods. The "control" method fits a global loess curve through a set of control spots, such as a whole-library titration series, and applies that curve to all the other spots.

More information on the loess control parameters span and iterations can be found under loessFit. The default values given here are equivalent to those for the older function in the SMA package.

The "robustspline" method calls normalizeRobustSpline.

MA.RG converts an unlogged RGList object into an MAList object. MA.RG(object) is equivalent to normalizeWithinArrays(object,method="none").

RG.MA(object) converts back from an MAList object to a RGList object with unlogged intensities.


An object of class MAList. Any components found in object will preserved except for R, G, Rb, Gb and other.


Gordon Smyth


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, Vol. 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.

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.

See Also

An overview of LIMMA functions for normalization is given in 05.Normalization.

See also normalizeBetweenArrays and maNorm in the marrayNorm package.


#  See normalizeBetweenArrays

[Package limma version 2.4.7 Index]