normalizeRobustSpline(M,A,layout=NULL,df=5,method="M")"M" for M-estimation or "MM" for high breakdown point regressionThe original motivation for the robustspline method was to use whole-array information to moderate the normalization curves used for the individual print-tip groups. This was an important issue for academically printed spotted two-color microarrays, especially when some of the print-tip groups contained relatively few spots. In these situations, robust spline normalization ensures stable results even for print-tip groups with few spots.
Modern commercial two colour arrays do not usually have print tips, so in effect the whole array is a single print-tip group, and so the need for moderating individual curves is gone.
Robustspline normalization can still be used for data from these arrays, in which case a single normalization curve is estimated.
In this situation, the method is closely analogous to global loess, with a regression spline replacing the loess curve and with robust
regression replacing the loess robustifying weights.
Robust spline normalization with method="MM" has potential advantages over global loess normalization when there a lot of differential expression or the differential expression is assymetric, because of the increased level of robustness.
The potential advantages of this approach have not been fully explored in a refereed publication however.
normalizeRobustSpline uses ns in the splines package to specify regression splines and rlm in the MASS package for robust regression.This function is usually accessed through normalizeWithinArrays.
An overview of LIMMA functions for normalization is given in 05.Normalization.
A <- 1:100
M <- rnorm(100)
normalized.M <- normalizeRobustSpline(M,A)
# Usual usage
## Not run: MA <- normalizeWithinArrays(RG, method="robustspline")
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