normalizeWithinArrays {limma} | R Documentation |
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") MA.RG(object, log.transform=TRUE) RG.MA(object)
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" 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" . |
log.transform |
logical indicating whether intensities should be log2 transformed.
If FALSE then intensities are assumed to be already logged. |
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.
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 stat.ma
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 intensities on the log2 scale.
An object of class MAList
.
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.
An overview of LIMMA functions for normalization is given in 4.Normalization.
See also normalizeBetweenArrays
and maNorm
in the marrayNorm package.
# See normalizeBetweenArrays