limma (version 3.28.14)

normalizeWithinArrays: Normalize Within Arrays

Description

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.

Usage

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)
RG.MA(object)

Arguments

object

object of class list, RGList or MAList containing red and green intensities constituting 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 containing spot quality weights.

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" or method="control".

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.

Value

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

Details

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. Method "none" computes M-values and A-values but does no normalization. Method "median" subtracts the weighted median from the M-values for each array.

The loess normalization methods ("loess", "printtiploess" and "composite") were proposed by Yang et al (2001, 2002). Smyth and Speed (2003) review these methods and describe how the methods are implemented in the limma package, including choices of tuning parameters. More information on the loess control parameters span and iterations can be found under loessFit. The default values used here are equivalent to those for the older function stat.ma in the sma package.

Oshlack et al (2004) consider the special issues that arise when a large proportion of probes are differentially expressed. They propose an improved version of composite loess normalization, which is implemented in the "control" method. This 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.

The "robustspline" method calls normalizeRobustSpline. See that function for more documentation.

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.

weights is normally a matrix giving a quality weight for every spot on every array. If weights is instead a vector or a matrix with only one column, then the weights will be assumed to be the same for every array, i.e., the weights will be probe-specific rather than spot-specific.

References

Oshlack, A., Emslie, D., Corcoran, L., and Smyth, G. K. (2007). Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes. Genome Biology 8, R2.

Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. Methods 31, 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, 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.

See Also

An overview of limma functions for normalization is given in 05.Normalization. In particular, see normalizeBetweenArrays for between-array normalization.

The original loess normalization function was the statma funtion in the sma package. normalizeWithinArrays is a direct generalization of that function, with more options and with support for quantitative spot quality weights.

A different implementation of loess normalization methods, with potentially different behavior, is provided by the maNorm in the marray package.